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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">Distributed Shared Memory System</h1><div class="u-tcGrayDark">565 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>Distributed Shared Memory System</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/Distributed_Shared_Memory_System">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_22945349" data-work_id="22945349" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/22945349/Data_and_Workload_Distribution_in_a_Multithreaded_Architecture">Data and Workload Distribution in a Multithreaded Architecture</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Matching data distribution to workload distribution is important to improve the performance of distributedmemory multiprocessors. While data and workload distribution can be tailored to fit a particular problem to a particular... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_22945349" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Matching data distribution to workload distribution is important to improve the performance of distributedmemory multiprocessors. While data and workload distribution can be tailored to fit a particular problem to a particular distributed-memory architecture, it is often difficult to do so for various reasons including complexity of address computation, runtime data movement, and irregular resource usage. This report presents our study on multithreading for distributed-memory multiprocessors. Specifically, we investigate the effects of multithreading on data distribution and workload distribution with variable thread granularity. Various types of workload distribution strategies are defined along with thread granularity. Several types of data distribution strategies are investigated. These include row-wise cyclic, k-way partial-row cyclic, and blocked distribution. To investigate the performance of multithreading, two problems are selected: highly sequential Gaussian Elimination with Partial Pivoting and highly parallel Matrix Multiplication. Execution results on the 80-processor EM-4 distributed-memory multiprocessor indicate that multithreading can offset the loss that is due to the mismatch between data distribution and workload distribution even for sequential and irregular problems while giving high absolute performance.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/22945349" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6f6ccbd998f92aa51d5b9bf79e71daae" rel="nofollow" data-download="{"attachment_id":43469979,"asset_id":22945349,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43469979/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="35342912" href="https://independent.academia.edu/JeanlucGaudiot">Jean-luc Gaudiot</a><script data-card-contents-for-user="35342912" type="text/json">{"id":35342912,"first_name":"Jean-luc","last_name":"Gaudiot","domain_name":"independent","page_name":"JeanlucGaudiot","display_name":"Jean-luc Gaudiot","profile_url":"https://independent.academia.edu/JeanlucGaudiot?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_22945349 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="22945349"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 22945349, container: ".js-paper-rank-work_22945349", }); });</script></li><li class="js-percentile-work_22945349 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 22945349; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_22945349"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_22945349 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="22945349"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 22945349; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=22945349]").text(description); $(".js-view-count-work_22945349").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_22945349").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="22945349"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="440" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="80870" rel="nofollow" href="https://www.academia.edu/Documents/in/Parallel_and_Distributed_Computing">Parallel & Distributed Computing</a>, <script data-card-contents-for-ri="80870" type="text/json">{"id":80870,"name":"Parallel \u0026 Distributed Computing","url":"https://www.academia.edu/Documents/in/Parallel_and_Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="346249" rel="nofollow" href="https://www.academia.edu/Documents/in/Matrix_Multiplication">Matrix Multiplication</a><script data-card-contents-for-ri="346249" type="text/json">{"id":346249,"name":"Matrix Multiplication","url":"https://www.academia.edu/Documents/in/Matrix_Multiplication?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=22945349]'), work: {"id":22945349,"title":"Data and Workload Distribution in a Multithreaded Architecture","created_at":"2016-03-07T12:07:44.436-08:00","url":"https://www.academia.edu/22945349/Data_and_Workload_Distribution_in_a_Multithreaded_Architecture?f_ri=14118","dom_id":"work_22945349","summary":"Matching data distribution to workload distribution is important to improve the performance of distributedmemory multiprocessors. While data and workload distribution can be tailored to fit a particular problem to a particular distributed-memory architecture, it is often difficult to do so for various reasons including complexity of address computation, runtime data movement, and irregular resource usage. This report presents our study on multithreading for distributed-memory multiprocessors. Specifically, we investigate the effects of multithreading on data distribution and workload distribution with variable thread granularity. Various types of workload distribution strategies are defined along with thread granularity. Several types of data distribution strategies are investigated. These include row-wise cyclic, k-way partial-row cyclic, and blocked distribution. To investigate the performance of multithreading, two problems are selected: highly sequential Gaussian Elimination with Partial Pivoting and highly parallel Matrix Multiplication. Execution results on the 80-processor EM-4 distributed-memory multiprocessor indicate that multithreading can offset the loss that is due to the mismatch between data distribution and workload distribution even for sequential and irregular problems while giving high absolute performance.","downloadable_attachments":[{"id":43469979,"asset_id":22945349,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":35342912,"first_name":"Jean-luc","last_name":"Gaudiot","domain_name":"independent","page_name":"JeanlucGaudiot","display_name":"Jean-luc Gaudiot","profile_url":"https://independent.academia.edu/JeanlucGaudiot?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":80870,"name":"Parallel \u0026 Distributed Computing","url":"https://www.academia.edu/Documents/in/Parallel_and_Distributed_Computing?f_ri=14118","nofollow":true},{"id":346249,"name":"Matrix Multiplication","url":"https://www.academia.edu/Documents/in/Matrix_Multiplication?f_ri=14118","nofollow":true},{"id":389519,"name":"Gaussian Elimination","url":"https://www.academia.edu/Documents/in/Gaussian_Elimination?f_ri=14118"},{"id":394067,"name":"Data Distribution","url":"https://www.academia.edu/Documents/in/Data_Distribution?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_68513506" data-work_id="68513506" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/68513506/Reconfigurable_interconnects_in_DSM_systems_a_focus_on_context_switch_behavior">Reconfigurable interconnects in DSM systems: a focus on context switch behavior</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Recent advances in the development of reconfigurable optical interconnect technologies allow for the fabrication of low cost and run-time adaptable interconnects in large distributed shared-memory (DSM) multiprocessor machines. This can... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_68513506" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Recent advances in the development of reconfigurable optical interconnect technologies allow for the fabrication of low cost and run-time adaptable interconnects in large distributed shared-memory (DSM) multiprocessor machines. This can allow the use of adaptable interconnection networks that alleviate the huge bottleneck present due to the gap between the processing speed and the memory access time over the network. In this paper we have studied the scheduling of tasks by the kernel of the operating system (OS) and its influence on communication between the processing nodes of the system, focusing on the traffic generated just after a context switch. We aim to use these results as a basis to propose a potential reconfiguration of the network that could provide a significant speedup.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/68513506" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="1b968d899978423d93e9ac0af9bfe7b6" rel="nofollow" data-download="{"attachment_id":78961125,"asset_id":68513506,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/78961125/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="58983156" href="https://independent.academia.edu/JDambre">J. Dambre</a><script data-card-contents-for-user="58983156" type="text/json">{"id":58983156,"first_name":"J.","last_name":"Dambre","domain_name":"independent","page_name":"JDambre","display_name":"J. Dambre","profile_url":"https://independent.academia.edu/JDambre?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_68513506 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="68513506"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 68513506, container: ".js-paper-rank-work_68513506", }); });</script></li><li class="js-percentile-work_68513506 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 68513506; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_68513506"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_68513506 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="68513506"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 68513506; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=68513506]").text(description); $(".js-view-count-work_68513506").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_68513506").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="68513506"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="44244" rel="nofollow" href="https://www.academia.edu/Documents/in/OPERATING_SYSTEM">OPERATING SYSTEM</a>, <script data-card-contents-for-ri="44244" type="text/json">{"id":44244,"name":"OPERATING SYSTEM","url":"https://www.academia.edu/Documents/in/OPERATING_SYSTEM?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="52437" rel="nofollow" href="https://www.academia.edu/Documents/in/Processing_Speed">Processing Speed</a>, <script data-card-contents-for-ri="52437" type="text/json">{"id":52437,"name":"Processing Speed","url":"https://www.academia.edu/Documents/in/Processing_Speed?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3691907" rel="nofollow" href="https://www.academia.edu/Documents/in/Shared_memory_multiprocessor_system">Shared memory multiprocessor system</a><script data-card-contents-for-ri="3691907" type="text/json">{"id":3691907,"name":"Shared memory multiprocessor system","url":"https://www.academia.edu/Documents/in/Shared_memory_multiprocessor_system?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=68513506]'), work: {"id":68513506,"title":"Reconfigurable interconnects in DSM systems: a focus on context switch behavior","created_at":"2022-01-17T03:43:11.206-08:00","url":"https://www.academia.edu/68513506/Reconfigurable_interconnects_in_DSM_systems_a_focus_on_context_switch_behavior?f_ri=14118","dom_id":"work_68513506","summary":"Recent advances in the development of reconfigurable optical interconnect technologies allow for the fabrication of low cost and run-time adaptable interconnects in large distributed shared-memory (DSM) multiprocessor machines. This can allow the use of adaptable interconnection networks that alleviate the huge bottleneck present due to the gap between the processing speed and the memory access time over the network. In this paper we have studied the scheduling of tasks by the kernel of the operating system (OS) and its influence on communication between the processing nodes of the system, focusing on the traffic generated just after a context switch. We aim to use these results as a basis to propose a potential reconfiguration of the network that could provide a significant speedup.","downloadable_attachments":[{"id":78961125,"asset_id":68513506,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":58983156,"first_name":"J.","last_name":"Dambre","domain_name":"independent","page_name":"JDambre","display_name":"J. Dambre","profile_url":"https://independent.academia.edu/JDambre?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":44244,"name":"OPERATING SYSTEM","url":"https://www.academia.edu/Documents/in/OPERATING_SYSTEM?f_ri=14118","nofollow":true},{"id":52437,"name":"Processing Speed","url":"https://www.academia.edu/Documents/in/Processing_Speed?f_ri=14118","nofollow":true},{"id":3691907,"name":"Shared memory multiprocessor system","url":"https://www.academia.edu/Documents/in/Shared_memory_multiprocessor_system?f_ri=14118","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_77036312" data-work_id="77036312" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/77036312/The_Genesis_distributed_memory_benchmarks_Part_1_Methodology_and_general_relativity_benchmark_with_results_for_the_SUPRENUM_computer">The Genesis distributed-memory benchmarks. Part 1: Methodology and general relativity benchmark with results for the SUPRENUM computer</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We study a model of spontaneous wavefunction collapse for a free quantum particle. We analyze in detail the time evolution of the single-Gaussian solution and the double-Gaussian solution, showing how the reduction mechanism induces the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_77036312" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We study a model of spontaneous wavefunction collapse for a free quantum particle. We analyze in detail the time evolution of the single-Gaussian solution and the double-Gaussian solution, showing how the reduction mechanism induces the localization of the wavefunction in space; we also study the asymptotic behavior of the general solution. With an appropriate choice for the parameter λ which sets the strength of the collapse mechanism, we prove that: i) the effects of the reducing terms on the dynamics of microscopic systems are negligible, the physical predictions of the model being very close to those of standard quantum mechanics; ii) at the macroscopic scale, the model reproduces classical mechanics: the wavefunction of the center of mass of a macro-object behaves, with high accuracy, like a point moving in space according to Newton's laws.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/77036312" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2d4fd9a83d24906b62160ecc5a0a1474" rel="nofollow" data-download="{"attachment_id":84544116,"asset_id":77036312,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/84544116/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="221333383" href="https://independent.academia.edu/NigelBishop3">Nigel Bishop</a><script data-card-contents-for-user="221333383" type="text/json">{"id":221333383,"first_name":"Nigel","last_name":"Bishop","domain_name":"independent","page_name":"NigelBishop3","display_name":"Nigel Bishop","profile_url":"https://independent.academia.edu/NigelBishop3?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_77036312 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="77036312"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 77036312, container: ".js-paper-rank-work_77036312", }); 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$(".js-view-count[data-work-id=77036312]").text(description); $(".js-view-count-work_77036312").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_77036312").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="77036312"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="440" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3504" rel="nofollow" href="https://www.academia.edu/Documents/in/General_Relativity">General Relativity</a>, <script data-card-contents-for-ri="3504" type="text/json">{"id":3504,"name":"General Relativity","url":"https://www.academia.edu/Documents/in/General_Relativity?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="64561" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Software">Computer Software</a><script data-card-contents-for-ri="64561" type="text/json">{"id":64561,"name":"Computer Software","url":"https://www.academia.edu/Documents/in/Computer_Software?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=77036312]'), work: {"id":77036312,"title":"The Genesis distributed-memory benchmarks. Part 1: Methodology and general relativity benchmark with results for the SUPRENUM computer","created_at":"2022-04-20T00:38:35.632-07:00","url":"https://www.academia.edu/77036312/The_Genesis_distributed_memory_benchmarks_Part_1_Methodology_and_general_relativity_benchmark_with_results_for_the_SUPRENUM_computer?f_ri=14118","dom_id":"work_77036312","summary":"We study a model of spontaneous wavefunction collapse for a free quantum particle. We analyze in detail the time evolution of the single-Gaussian solution and the double-Gaussian solution, showing how the reduction mechanism induces the localization of the wavefunction in space; we also study the asymptotic behavior of the general solution. With an appropriate choice for the parameter λ which sets the strength of the collapse mechanism, we prove that: i) the effects of the reducing terms on the dynamics of microscopic systems are negligible, the physical predictions of the model being very close to those of standard quantum mechanics; ii) at the macroscopic scale, the model reproduces classical mechanics: the wavefunction of the center of mass of a macro-object behaves, with high accuracy, like a point moving in space according to Newton's laws.","downloadable_attachments":[{"id":84544116,"asset_id":77036312,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":221333383,"first_name":"Nigel","last_name":"Bishop","domain_name":"independent","page_name":"NigelBishop3","display_name":"Nigel Bishop","profile_url":"https://independent.academia.edu/NigelBishop3?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":3504,"name":"General Relativity","url":"https://www.academia.edu/Documents/in/General_Relativity?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":64561,"name":"Computer Software","url":"https://www.academia.edu/Documents/in/Computer_Software?f_ri=14118","nofollow":true},{"id":70648,"name":"Concurrency","url":"https://www.academia.edu/Documents/in/Concurrency?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_50112922" data-work_id="50112922" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/50112922/Applications_for_the_scalable_coherent_interface">Applications for the scalable coherent interface</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">IEEE P1596, the Scalable Coherent Interface (formerly known as SuperBus) is based on experience gained while developing Fastbus (ANSI/IEEE 960-1986, IEC 935), Futurebus (IEEE P896.x) and other modern high-performance buses. SC1 goals... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_50112922" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">IEEE P1596, the Scalable Coherent Interface (formerly known as SuperBus) is based on experience gained while developing Fastbus (ANSI/IEEE 960-1986, IEC 935), Futurebus (IEEE P896.x) and other modern high-performance buses. SC1 goals include a minimum bandwidth of 1 GByte/sec per processor in multiprocessor systems with thousands of processors; efficient support of a coherent distributed-cache image of distributed shared memory; support for bridges which interface to existing or future buses; and support for inexpensive small rings as well as for general switched interconnections like Banyan, Omega, or crossbar networks. This paper reports the status of the work in progress and suggests some applications in data acquisition and physics.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/50112922" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5ec7610302ba8359f47852e38c6b5814" rel="nofollow" data-download="{"attachment_id":68220227,"asset_id":50112922,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/68220227/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7582568" href="https://independent.academia.edu/DavidGustavson">David Gustavson</a><script data-card-contents-for-user="7582568" type="text/json">{"id":7582568,"first_name":"David","last_name":"Gustavson","domain_name":"independent","page_name":"DavidGustavson","display_name":"David Gustavson","profile_url":"https://independent.academia.edu/DavidGustavson?f_ri=14118","photo":"https://0.academia-photos.com/7582568/165575355/155430342/s65_david.gustavson.jpeg"}</script></span></span></li><li class="js-paper-rank-work_50112922 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="50112922"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 50112922, container: ".js-paper-rank-work_50112922", }); 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$(".js-view-count[data-work-id=50112922]").text(description); $(".js-view-count-work_50112922").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_50112922").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="50112922"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="433" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Architecture">Computer Architecture</a>, <script data-card-contents-for-ri="433" type="text/json">{"id":433,"name":"Computer Architecture","url":"https://www.academia.edu/Documents/in/Computer_Architecture?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14024" rel="nofollow" href="https://www.academia.edu/Documents/in/High_Energy_Physics">High Energy Physics</a>, <script data-card-contents-for-ri="14024" type="text/json">{"id":14024,"name":"High Energy Physics","url":"https://www.academia.edu/Documents/in/High_Energy_Physics?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="104336" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_acquisition">Data acquisition</a><script data-card-contents-for-ri="104336" type="text/json">{"id":104336,"name":"Data acquisition","url":"https://www.academia.edu/Documents/in/Data_acquisition?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=50112922]'), work: {"id":50112922,"title":"Applications for the scalable coherent interface","created_at":"2021-07-20T14:16:08.232-07:00","url":"https://www.academia.edu/50112922/Applications_for_the_scalable_coherent_interface?f_ri=14118","dom_id":"work_50112922","summary":"IEEE P1596, the Scalable Coherent Interface (formerly known as SuperBus) is based on experience gained while developing Fastbus (ANSI/IEEE 960-1986, IEC 935), Futurebus (IEEE P896.x) and other modern high-performance buses. SC1 goals include a minimum bandwidth of 1 GByte/sec per processor in multiprocessor systems with thousands of processors; efficient support of a coherent distributed-cache image of distributed shared memory; support for bridges which interface to existing or future buses; and support for inexpensive small rings as well as for general switched interconnections like Banyan, Omega, or crossbar networks. This paper reports the status of the work in progress and suggests some applications in data acquisition and physics.","downloadable_attachments":[{"id":68220227,"asset_id":50112922,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7582568,"first_name":"David","last_name":"Gustavson","domain_name":"independent","page_name":"DavidGustavson","display_name":"David Gustavson","profile_url":"https://independent.academia.edu/DavidGustavson?f_ri=14118","photo":"https://0.academia-photos.com/7582568/165575355/155430342/s65_david.gustavson.jpeg"}],"research_interests":[{"id":433,"name":"Computer Architecture","url":"https://www.academia.edu/Documents/in/Computer_Architecture?f_ri=14118","nofollow":true},{"id":14024,"name":"High Energy Physics","url":"https://www.academia.edu/Documents/in/High_Energy_Physics?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":104336,"name":"Data acquisition","url":"https://www.academia.edu/Documents/in/Data_acquisition?f_ri=14118","nofollow":true},{"id":128014,"name":"Work in Progress","url":"https://www.academia.edu/Documents/in/Work_in_Progress?f_ri=14118"},{"id":1489846,"name":"Data Acquisition System","url":"https://www.academia.edu/Documents/in/Data_Acquisition_System?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_33323028" data-work_id="33323028" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/33323028/Distributed_Rendering_Engine">Distributed Rendering Engine</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper present aspects of architecture of a cluster of workstations developed using ATM and FastEthernet technology and some of the basic principles of distributed memory programming, based on message-passing. The team used an... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_33323028" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper present aspects of architecture of a cluster of workstations developed using ATM and FastEthernet technology and some of the basic principles of distributed memory programming, based on message-passing. The team used an application called Parallel POV-Ray rendering engine to show the viability of the "PoliCluster". This paper will describe the performance improvement that the Cluster architecture brought to this particular application. A significant role in choosing this particular application as an example was the natural parallelism of the rendering engine.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/33323028" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8d13ad3cdb00579c1e161f72a695e6a4" rel="nofollow" data-download="{"attachment_id":53384812,"asset_id":33323028,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/53384812/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="65121912" href="https://independent.academia.edu/TPopescu2">T. Popescu</a><script data-card-contents-for-user="65121912" type="text/json">{"id":65121912,"first_name":"T.","last_name":"Popescu","domain_name":"independent","page_name":"TPopescu2","display_name":"T. Popescu","profile_url":"https://independent.academia.edu/TPopescu2?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_33323028 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="33323028"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 33323028, container: ".js-paper-rank-work_33323028", }); });</script></li><li class="js-percentile-work_33323028 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33323028; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_33323028"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_33323028 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="33323028"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33323028; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33323028]").text(description); $(".js-view-count-work_33323028").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_33323028").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="33323028"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="137957" rel="nofollow" href="https://www.academia.edu/Documents/in/Performance_Improvement">Performance Improvement</a>, <script data-card-contents-for-ri="137957" type="text/json">{"id":137957,"name":"Performance Improvement","url":"https://www.academia.edu/Documents/in/Performance_Improvement?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="243826" rel="nofollow" href="https://www.academia.edu/Documents/in/Message_Passing">Message Passing</a><script data-card-contents-for-ri="243826" type="text/json">{"id":243826,"name":"Message Passing","url":"https://www.academia.edu/Documents/in/Message_Passing?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=33323028]'), work: {"id":33323028,"title":"Distributed Rendering Engine","created_at":"2017-06-04T07:23:04.863-07:00","url":"https://www.academia.edu/33323028/Distributed_Rendering_Engine?f_ri=14118","dom_id":"work_33323028","summary":"This paper present aspects of architecture of a cluster of workstations developed using ATM and FastEthernet technology and some of the basic principles of distributed memory programming, based on message-passing. The team used an application called Parallel POV-Ray rendering engine to show the viability of the \"PoliCluster\". This paper will describe the performance improvement that the Cluster architecture brought to this particular application. A significant role in choosing this particular application as an example was the natural parallelism of the rendering engine.","downloadable_attachments":[{"id":53384812,"asset_id":33323028,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":65121912,"first_name":"T.","last_name":"Popescu","domain_name":"independent","page_name":"TPopescu2","display_name":"T. Popescu","profile_url":"https://independent.academia.edu/TPopescu2?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":137957,"name":"Performance Improvement","url":"https://www.academia.edu/Documents/in/Performance_Improvement?f_ri=14118","nofollow":true},{"id":243826,"name":"Message Passing","url":"https://www.academia.edu/Documents/in/Message_Passing?f_ri=14118","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_69278220" data-work_id="69278220" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/69278220/Distributed_Shared_Memory_on_Loosely_Coupled_Systems">Distributed Shared Memory on Loosely Coupled Systems</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The distributed shared memory model (DSMM) is considered a feasible alternative to the traditional communication model (CM), especiallv in loosely coupled distributed sijstems. While the CM is usually considered a low-level model, the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_69278220" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The distributed shared memory model (DSMM) is considered a feasible alternative to the traditional communication model (CM), especiallv in loosely coupled distributed sijstems. While the CM is usually considered a low-level model, the DSMM provides a shared address space that can be used in the same way as local memory. This paper provides a taxonomy of distributed shared memory systems, focusing on different implementations and the factors which affect the behavior of those implementations.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/69278220" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f4a7188f34df7b7c82a361f33aaae61e" rel="nofollow" data-download="{"attachment_id":79435111,"asset_id":69278220,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/79435111/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="14247602" href="https://illinois.academia.edu/RoyCampbell">Roy Campbell</a><script data-card-contents-for-user="14247602" type="text/json">{"id":14247602,"first_name":"Roy","last_name":"Campbell","domain_name":"illinois","page_name":"RoyCampbell","display_name":"Roy Campbell","profile_url":"https://illinois.academia.edu/RoyCampbell?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_69278220 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="69278220"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 69278220, container: ".js-paper-rank-work_69278220", }); });</script></li><li class="js-percentile-work_69278220 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 69278220; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_69278220"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_69278220 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="69278220"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 69278220; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=69278220]").text(description); $(".js-view-count-work_69278220").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_69278220").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="69278220"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="55476" rel="nofollow" href="https://www.academia.edu/Documents/in/Informatica">Informatica</a><script data-card-contents-for-ri="55476" type="text/json">{"id":55476,"name":"Informatica","url":"https://www.academia.edu/Documents/in/Informatica?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=69278220]'), work: {"id":69278220,"title":"Distributed Shared Memory on Loosely Coupled Systems","created_at":"2022-01-23T12:42:04.567-08:00","url":"https://www.academia.edu/69278220/Distributed_Shared_Memory_on_Loosely_Coupled_Systems?f_ri=14118","dom_id":"work_69278220","summary":"The distributed shared memory model (DSMM) is considered a feasible alternative to the traditional communication model (CM), especiallv in loosely coupled distributed sijstems. While the CM is usually considered a low-level model, the DSMM provides a shared address space that can be used in the same way as local memory. This paper provides a taxonomy of distributed shared memory systems, focusing on different implementations and the factors which affect the behavior of those implementations.","downloadable_attachments":[{"id":79435111,"asset_id":69278220,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":14247602,"first_name":"Roy","last_name":"Campbell","domain_name":"illinois","page_name":"RoyCampbell","display_name":"Roy Campbell","profile_url":"https://illinois.academia.edu/RoyCampbell?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":55476,"name":"Informatica","url":"https://www.academia.edu/Documents/in/Informatica?f_ri=14118","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_67723538" data-work_id="67723538" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/67723538/P_ARPACK_An_efficient_portable_large_scale_eigenvalue_package_for_distributed_memory_parallel_architectures">P_ARPACK: An efficient portable large scale eigenvalue package for distributed memory parallel architectures</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">P ARPACK is a parallel version of the ARPACK software. ARPACK is a package of Fortran 77 subroutines which implement the Implicitly Restarted Arnoldi Method used for solving large sparse eigenvalue problems. A parallel implementation of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_67723538" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">P ARPACK is a parallel version of the ARPACK software. ARPACK is a package of Fortran 77 subroutines which implement the Implicitly Restarted Arnoldi Method used for solving large sparse eigenvalue problems. A parallel implementation of ARPACK is presented which is portable across a wide range of distributed memory platforms and requires minimal changes to the serial code. The communication layers used for message passing are the Basic Linear Algebra Communication Subprograms (BLACS) developed for the ScaLAPACK project and Message Passing Interface(MPI).</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/67723538" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6695a992585cedc8e2f473d8245d7841" rel="nofollow" data-download="{"attachment_id":78444855,"asset_id":67723538,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/78444855/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="6155050" href="https://cranfield.academia.edu/SalvatoreFilippone">Salvatore Filippone</a><script data-card-contents-for-user="6155050" type="text/json">{"id":6155050,"first_name":"Salvatore","last_name":"Filippone","domain_name":"cranfield","page_name":"SalvatoreFilippone","display_name":"Salvatore Filippone","profile_url":"https://cranfield.academia.edu/SalvatoreFilippone?f_ri=14118","photo":"https://0.academia-photos.com/6155050/2556970/2969362/s65_salvatore.filippone.jpg"}</script></span></span></li><li class="js-paper-rank-work_67723538 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="67723538"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 67723538, container: ".js-paper-rank-work_67723538", }); });</script></li><li class="js-percentile-work_67723538 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67723538; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_67723538"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_67723538 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="67723538"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67723538; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67723538]").text(description); $(".js-view-count-work_67723538").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_67723538").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="67723538"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="29972" rel="nofollow" href="https://www.academia.edu/Documents/in/Linear_Algebra">Linear Algebra</a>, <script data-card-contents-for-ri="29972" type="text/json">{"id":29972,"name":"Linear Algebra","url":"https://www.academia.edu/Documents/in/Linear_Algebra?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="70448" rel="nofollow" href="https://www.academia.edu/Documents/in/Fortran">Fortran</a><script data-card-contents-for-ri="70448" type="text/json">{"id":70448,"name":"Fortran","url":"https://www.academia.edu/Documents/in/Fortran?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=67723538]'), work: {"id":67723538,"title":"P_ARPACK: An efficient portable large scale eigenvalue package for distributed memory parallel architectures","created_at":"2022-01-09T23:55:09.899-08:00","url":"https://www.academia.edu/67723538/P_ARPACK_An_efficient_portable_large_scale_eigenvalue_package_for_distributed_memory_parallel_architectures?f_ri=14118","dom_id":"work_67723538","summary":"P ARPACK is a parallel version of the ARPACK software. ARPACK is a package of Fortran 77 subroutines which implement the Implicitly Restarted Arnoldi Method used for solving large sparse eigenvalue problems. A parallel implementation of ARPACK is presented which is portable across a wide range of distributed memory platforms and requires minimal changes to the serial code. The communication layers used for message passing are the Basic Linear Algebra Communication Subprograms (BLACS) developed for the ScaLAPACK project and Message Passing Interface(MPI).","downloadable_attachments":[{"id":78444855,"asset_id":67723538,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":6155050,"first_name":"Salvatore","last_name":"Filippone","domain_name":"cranfield","page_name":"SalvatoreFilippone","display_name":"Salvatore Filippone","profile_url":"https://cranfield.academia.edu/SalvatoreFilippone?f_ri=14118","photo":"https://0.academia-photos.com/6155050/2556970/2969362/s65_salvatore.filippone.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":29972,"name":"Linear Algebra","url":"https://www.academia.edu/Documents/in/Linear_Algebra?f_ri=14118","nofollow":true},{"id":70448,"name":"Fortran","url":"https://www.academia.edu/Documents/in/Fortran?f_ri=14118","nofollow":true},{"id":224852,"name":"Eigenvalues","url":"https://www.academia.edu/Documents/in/Eigenvalues?f_ri=14118"},{"id":243826,"name":"Message Passing","url":"https://www.academia.edu/Documents/in/Message_Passing?f_ri=14118"},{"id":758278,"name":"Large Scale","url":"https://www.academia.edu/Documents/in/Large_Scale?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_63777722" data-work_id="63777722" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/63777722/Scheduling_pipelined_communication_in_distributed_memory_multiprocessors_for_real_time_applications">Scheduling pipelined communication in distributed memory multiprocessors for real-time applications</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper investigates communication in distributed memory multiprocessors to support tasklevel parallelism for real-time applications. It is shown that wormhole routing, used in second generation multicomputers, does not support... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_63777722" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper investigates communication in distributed memory multiprocessors to support tasklevel parallelism for real-time applications. It is shown that wormhole routing, used in second generation multicomputers, does not support task-level pipelining because its oblivious contention resolution leads to output inconsistency in which a constant throughput is not guaranteed. We propose scheduled routing which guarantees constant throughputs by integrating task specifications with flow-control. In this routing technique, communication processors provide explicit flowcontrol by independently executing switching schedules computed at compile-time. It is deadlock-free, contention-free, does not load the intermediate node memory, and makes use of the multiple equivalent paths between non-adjacent nodes. The resource allocation and scheduling problems resulting from such routing are formulated and related implementation issues are anal yzed. A comparison with wormhole routing for various generalized hyp ercubes and tori shows that scheduled routing is effective in providing a constant throughput when wormhole routing does not and enables pipelining at higher input arrival rates.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/63777722" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8860d74ea02d05a50a006dff3e911260" rel="nofollow" data-download="{"attachment_id":76088793,"asset_id":63777722,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/76088793/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="188296234" href="https://independent.academia.edu/ShridharShukla4">Shridhar Shukla</a><script data-card-contents-for-user="188296234" type="text/json">{"id":188296234,"first_name":"Shridhar","last_name":"Shukla","domain_name":"independent","page_name":"ShridharShukla4","display_name":"Shridhar Shukla","profile_url":"https://independent.academia.edu/ShridharShukla4?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_63777722 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="63777722"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 63777722, container: ".js-paper-rank-work_63777722", }); 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$(".js-view-count[data-work-id=63777722]").text(description); $(".js-view-count-work_63777722").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_63777722").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="63777722"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">10</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="440" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="7789" rel="nofollow" href="https://www.academia.edu/Documents/in/Routing">Routing</a>, <script data-card-contents-for-ri="7789" type="text/json">{"id":7789,"name":"Routing","url":"https://www.academia.edu/Documents/in/Routing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14604" rel="nofollow" href="https://www.academia.edu/Documents/in/Flow_Control">Flow Control</a><script data-card-contents-for-ri="14604" type="text/json">{"id":14604,"name":"Flow Control","url":"https://www.academia.edu/Documents/in/Flow_Control?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=63777722]'), work: {"id":63777722,"title":"Scheduling pipelined communication in distributed memory multiprocessors for real-time applications","created_at":"2021-12-10T07:29:28.086-08:00","url":"https://www.academia.edu/63777722/Scheduling_pipelined_communication_in_distributed_memory_multiprocessors_for_real_time_applications?f_ri=14118","dom_id":"work_63777722","summary":"This paper investigates communication in distributed memory multiprocessors to support tasklevel parallelism for real-time applications. It is shown that wormhole routing, used in second generation multicomputers, does not support task-level pipelining because its oblivious contention resolution leads to output inconsistency in which a constant throughput is not guaranteed. We propose scheduled routing which guarantees constant throughputs by integrating task specifications with flow-control. In this routing technique, communication processors provide explicit flowcontrol by independently executing switching schedules computed at compile-time. It is deadlock-free, contention-free, does not load the intermediate node memory, and makes use of the multiple equivalent paths between non-adjacent nodes. The resource allocation and scheduling problems resulting from such routing are formulated and related implementation issues are anal yzed. A comparison with wormhole routing for various generalized hyp ercubes and tori shows that scheduled routing is effective in providing a constant throughput when wormhole routing does not and enables pipelining at higher input arrival rates.","downloadable_attachments":[{"id":76088793,"asset_id":63777722,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":188296234,"first_name":"Shridhar","last_name":"Shukla","domain_name":"independent","page_name":"ShridharShukla4","display_name":"Shridhar Shukla","profile_url":"https://independent.academia.edu/ShridharShukla4?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":7789,"name":"Routing","url":"https://www.academia.edu/Documents/in/Routing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":14604,"name":"Flow Control","url":"https://www.academia.edu/Documents/in/Flow_Control?f_ri=14118","nofollow":true},{"id":102883,"name":"Real Time Systems","url":"https://www.academia.edu/Documents/in/Real_Time_Systems?f_ri=14118"},{"id":198557,"name":"Throughput","url":"https://www.academia.edu/Documents/in/Throughput?f_ri=14118"},{"id":364020,"name":"Real Time Application","url":"https://www.academia.edu/Documents/in/Real_Time_Application?f_ri=14118"},{"id":1931321,"name":"Application Software","url":"https://www.academia.edu/Documents/in/Application_Software?f_ri=14118"},{"id":1952469,"name":"Wormhole routing","url":"https://www.academia.edu/Documents/in/Wormhole_routing?f_ri=14118"},{"id":2801643,"name":"Concurrent Computing","url":"https://www.academia.edu/Documents/in/Concurrent_Computing?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_8194895" data-work_id="8194895" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/8194895/Hardware_Prefetching_in_Bus_Based_Multiprocessors_Pattern_Characterization_and_Cost_Effective_Hardware">Hardware Prefetching in Bus-Based Multiprocessors: Pattern Characterization and Cost-Effective Hardware</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Data prefetching has been widely studied as a technique to hide memory access latency in multiprocessors. Most recent research on hardware prefetching focuses either on uniprocessors, or on distributed shared memory (DSM) and other non... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_8194895" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Data prefetching has been widely studied as a technique to hide memory access latency in multiprocessors. Most recent research on hardware prefetching focuses either on uniprocessors, or on distributed shared memory (DSM) and other non bus-based organizations. However, in the context of bus-based SMPs, prefetching poses a number of problems related to the lack of scalability and limited bus bandwidth of these modestsized machines. This paper considers how the number of processors and the memory access patterns in the program influence the relative performance of sequential and non-sequential prefetching mechanisms in a bus-based SMP. We compare the performance of four inexpensive hardware prefetching techniques, varying the number of processors. After a breakdown of the results based on a performance model, we propose a cost-effective hardware prefetching solution for implementing on such modest-sized multiprocessors.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/8194895" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6b84c5f758d5b27e62f5df6488189662" rel="nofollow" data-download="{"attachment_id":48187828,"asset_id":8194895,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/48187828/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="16058781" href="https://unizar.academia.edu/VictorVi%C3%B1als">Victor Viñals</a><script data-card-contents-for-user="16058781" type="text/json">{"id":16058781,"first_name":"Victor","last_name":"Viñals","domain_name":"unizar","page_name":"VictorViñals","display_name":"Victor Viñals","profile_url":"https://unizar.academia.edu/VictorVi%C3%B1als?f_ri=14118","photo":"https://0.academia-photos.com/16058781/163609505/153404226/s65_victor.vi_als.png"}</script></span></span></li><li class="js-paper-rank-work_8194895 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="8194895"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 8194895, container: ".js-paper-rank-work_8194895", }); });</script></li><li class="js-percentile-work_8194895 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 8194895; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_8194895"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_8194895 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="8194895"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 8194895; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=8194895]").text(description); $(".js-view-count-work_8194895").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_8194895").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="8194895"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="113434" rel="nofollow" href="https://www.academia.edu/Documents/in/Performance_Model">Performance Model</a>, <script data-card-contents-for-ri="113434" type="text/json">{"id":113434,"name":"Performance Model","url":"https://www.academia.edu/Documents/in/Performance_Model?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="165565" rel="nofollow" href="https://www.academia.edu/Documents/in/Cost_effectiveness">Cost effectiveness</a>, <script data-card-contents-for-ri="165565" type="text/json">{"id":165565,"name":"Cost effectiveness","url":"https://www.academia.edu/Documents/in/Cost_effectiveness?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="186101" rel="nofollow" href="https://www.academia.edu/Documents/in/Random_access_memory">Random access memory</a><script data-card-contents-for-ri="186101" type="text/json">{"id":186101,"name":"Random access memory","url":"https://www.academia.edu/Documents/in/Random_access_memory?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=8194895]'), work: {"id":8194895,"title":"Hardware Prefetching in Bus-Based Multiprocessors: Pattern Characterization and Cost-Effective Hardware","created_at":"2014-09-04T01:16:06.996-07:00","url":"https://www.academia.edu/8194895/Hardware_Prefetching_in_Bus_Based_Multiprocessors_Pattern_Characterization_and_Cost_Effective_Hardware?f_ri=14118","dom_id":"work_8194895","summary":"Data prefetching has been widely studied as a technique to hide memory access latency in multiprocessors. Most recent research on hardware prefetching focuses either on uniprocessors, or on distributed shared memory (DSM) and other non bus-based organizations. However, in the context of bus-based SMPs, prefetching poses a number of problems related to the lack of scalability and limited bus bandwidth of these modestsized machines. This paper considers how the number of processors and the memory access patterns in the program influence the relative performance of sequential and non-sequential prefetching mechanisms in a bus-based SMP. We compare the performance of four inexpensive hardware prefetching techniques, varying the number of processors. After a breakdown of the results based on a performance model, we propose a cost-effective hardware prefetching solution for implementing on such modest-sized multiprocessors.","downloadable_attachments":[{"id":48187828,"asset_id":8194895,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":16058781,"first_name":"Victor","last_name":"Viñals","domain_name":"unizar","page_name":"VictorViñals","display_name":"Victor Viñals","profile_url":"https://unizar.academia.edu/VictorVi%C3%B1als?f_ri=14118","photo":"https://0.academia-photos.com/16058781/163609505/153404226/s65_victor.vi_als.png"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":113434,"name":"Performance Model","url":"https://www.academia.edu/Documents/in/Performance_Model?f_ri=14118","nofollow":true},{"id":165565,"name":"Cost effectiveness","url":"https://www.academia.edu/Documents/in/Cost_effectiveness?f_ri=14118","nofollow":true},{"id":186101,"name":"Random access memory","url":"https://www.academia.edu/Documents/in/Random_access_memory?f_ri=14118","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_21778762" data-work_id="21778762" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/21778762/Block_Structured_Multigrid_for_the_Navier_Stokes_Equations_Experiences_and_Scalability_Questions">Block-Structured Multigrid for the Navier-Stokes Equations: Experiences and Scalability Questions</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper summarizes investigations concerning the algorithmic scalability of multigrid methods for partial di erential equations on MIMD distributed memory systems. It is shown that even multigrid methods which are distinguished by... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_21778762" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper summarizes investigations concerning the algorithmic scalability of multigrid methods for partial di erential equations on MIMD distributed memory systems. It is shown that even multigrid methods which are distinguished by h-independent convergence rates are not scalable in a rigorous sense. We develop their parallel asymptotic computational complexity for di erent types of multigrid cycles and analyze their critical components with respect to scalability. Experimental results for two Navier-Stokes test problems presented in the last section of this paper show, however, that the theoretically predicted dependency of the combined numerical and parallel e ciencies of multigrid methods on the number of processors employed is in fact very weak. This leads to the conclusion that multigrid is also with respect to scalability an appropriate candidate for solving partial di erential equations on massively parallel machines.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/21778762" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8c735f74fc39d48d131f28f6af6e6c95" rel="nofollow" data-download="{"attachment_id":42544768,"asset_id":21778762,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42544768/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="42993191" href="https://independent.academia.edu/HubertRitzdorf">Hubert Ritzdorf</a><script data-card-contents-for-user="42993191" type="text/json">{"id":42993191,"first_name":"Hubert","last_name":"Ritzdorf","domain_name":"independent","page_name":"HubertRitzdorf","display_name":"Hubert Ritzdorf","profile_url":"https://independent.academia.edu/HubertRitzdorf?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_21778762 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="21778762"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 21778762, container: ".js-paper-rank-work_21778762", }); 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$(".js-view-count[data-work-id=21778762]").text(description); $(".js-view-count-work_21778762").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_21778762").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="21778762"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="166907" rel="nofollow" href="https://www.academia.edu/Documents/in/Convergence_Rate">Convergence Rate</a>, <script data-card-contents-for-ri="166907" type="text/json">{"id":166907,"name":"Convergence Rate","url":"https://www.academia.edu/Documents/in/Convergence_Rate?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="194130" rel="nofollow" href="https://www.academia.edu/Documents/in/PARTIAL_DIFFERENTIAL_EQUATION">PARTIAL DIFFERENTIAL EQUATION</a><script data-card-contents-for-ri="194130" type="text/json">{"id":194130,"name":"PARTIAL DIFFERENTIAL EQUATION","url":"https://www.academia.edu/Documents/in/PARTIAL_DIFFERENTIAL_EQUATION?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=21778762]'), work: {"id":21778762,"title":"Block-Structured Multigrid for the Navier-Stokes Equations: Experiences and Scalability Questions","created_at":"2016-02-10T08:22:12.797-08:00","url":"https://www.academia.edu/21778762/Block_Structured_Multigrid_for_the_Navier_Stokes_Equations_Experiences_and_Scalability_Questions?f_ri=14118","dom_id":"work_21778762","summary":"This paper summarizes investigations concerning the algorithmic scalability of multigrid methods for partial di erential equations on MIMD distributed memory systems. It is shown that even multigrid methods which are distinguished by h-independent convergence rates are not scalable in a rigorous sense. We develop their parallel asymptotic computational complexity for di erent types of multigrid cycles and analyze their critical components with respect to scalability. Experimental results for two Navier-Stokes test problems presented in the last section of this paper show, however, that the theoretically predicted dependency of the combined numerical and parallel e ciencies of multigrid methods on the number of processors employed is in fact very weak. This leads to the conclusion that multigrid is also with respect to scalability an appropriate candidate for solving partial di erential equations on massively parallel machines.","downloadable_attachments":[{"id":42544768,"asset_id":21778762,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":42993191,"first_name":"Hubert","last_name":"Ritzdorf","domain_name":"independent","page_name":"HubertRitzdorf","display_name":"Hubert Ritzdorf","profile_url":"https://independent.academia.edu/HubertRitzdorf?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":166907,"name":"Convergence Rate","url":"https://www.academia.edu/Documents/in/Convergence_Rate?f_ri=14118","nofollow":true},{"id":194130,"name":"PARTIAL DIFFERENTIAL EQUATION","url":"https://www.academia.edu/Documents/in/PARTIAL_DIFFERENTIAL_EQUATION?f_ri=14118","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_19479606" data-work_id="19479606" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/19479606/Automatic_synthesis_of_cache_coherence_protocol_processors_using_Bluespec">Automatic synthesis of cache-coherence protocol processors using Bluespec</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">There are few published examples of the proof of correctness of a cache-coherence protocol expressed in an HDL. A designer generally shows the correctness of a protocol where many implementation details have been abstracted away. Abstract... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_19479606" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">There are few published examples of the proof of correctness of a cache-coherence protocol expressed in an HDL. A designer generally shows the correctness of a protocol where many implementation details have been abstracted away. Abstract protocols are often expressed as a table of rules or state transition diagrams with an (implicit) model of atomic actions. There is enough of a semantic gap between these high-level abstract descriptions and HDLs that the task of showing the correctness of an implementation of a verified abstract protocol is as daunting as proving the correctness of the abstract protocol in the first place. The main contribution of this paper is to show that 1. it is straightforward to express these protocols in Bluespec SystemVerilog (BSV), a hardware description language based on guarded atomic actions, and 2. it is possible to synthesize an hardware implementation automatically from such a description using the BSV compiler. Consequently, once a protocol has been verified at the rules-level, little verification effort is needed to verify the implementation. We illustrate our approach by synthesizing a non-blocking MSI cache-coherence protocol for Distributed Memory Systems and discuss the performance of the resulting implementation.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/19479606" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="19de7d4ee0f2c1c952028a4e92984731" rel="nofollow" data-download="{"attachment_id":40643084,"asset_id":19479606,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40643084/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="39752081" href="https://independent.academia.edu/ArvindArvind4">Arvind Arvind</a><script data-card-contents-for-user="39752081" type="text/json">{"id":39752081,"first_name":"Arvind","last_name":"Arvind","domain_name":"independent","page_name":"ArvindArvind4","display_name":"Arvind Arvind","profile_url":"https://independent.academia.edu/ArvindArvind4?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_19479606 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="19479606"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 19479606, container: ".js-paper-rank-work_19479606", }); });</script></li><li class="js-percentile-work_19479606 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19479606; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_19479606"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_19479606 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="19479606"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19479606; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19479606]").text(description); $(".js-view-count-work_19479606").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_19479606").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="19479606"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="413301" rel="nofollow" href="https://www.academia.edu/Documents/in/Perforation">Perforation</a>, <script data-card-contents-for-ri="413301" type="text/json">{"id":413301,"name":"Perforation","url":"https://www.academia.edu/Documents/in/Perforation?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="535969" rel="nofollow" href="https://www.academia.edu/Documents/in/Hardware_Implementation_of_Algorithms">Hardware Implementation of Algorithms</a><script data-card-contents-for-ri="535969" type="text/json">{"id":535969,"name":"Hardware Implementation of Algorithms","url":"https://www.academia.edu/Documents/in/Hardware_Implementation_of_Algorithms?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=19479606]'), work: {"id":19479606,"title":"Automatic synthesis of cache-coherence protocol processors using Bluespec","created_at":"2015-12-04T12:23:09.667-08:00","url":"https://www.academia.edu/19479606/Automatic_synthesis_of_cache_coherence_protocol_processors_using_Bluespec?f_ri=14118","dom_id":"work_19479606","summary":"There are few published examples of the proof of correctness of a cache-coherence protocol expressed in an HDL. A designer generally shows the correctness of a protocol where many implementation details have been abstracted away. Abstract protocols are often expressed as a table of rules or state transition diagrams with an (implicit) model of atomic actions. There is enough of a semantic gap between these high-level abstract descriptions and HDLs that the task of showing the correctness of an implementation of a verified abstract protocol is as daunting as proving the correctness of the abstract protocol in the first place. The main contribution of this paper is to show that 1. it is straightforward to express these protocols in Bluespec SystemVerilog (BSV), a hardware description language based on guarded atomic actions, and 2. it is possible to synthesize an hardware implementation automatically from such a description using the BSV compiler. Consequently, once a protocol has been verified at the rules-level, little verification effort is needed to verify the implementation. We illustrate our approach by synthesizing a non-blocking MSI cache-coherence protocol for Distributed Memory Systems and discuss the performance of the resulting implementation.","downloadable_attachments":[{"id":40643084,"asset_id":19479606,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":39752081,"first_name":"Arvind","last_name":"Arvind","domain_name":"independent","page_name":"ArvindArvind4","display_name":"Arvind Arvind","profile_url":"https://independent.academia.edu/ArvindArvind4?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":413301,"name":"Perforation","url":"https://www.academia.edu/Documents/in/Perforation?f_ri=14118","nofollow":true},{"id":535969,"name":"Hardware Implementation of Algorithms","url":"https://www.academia.edu/Documents/in/Hardware_Implementation_of_Algorithms?f_ri=14118","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_52054343" data-work_id="52054343" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/52054343/Scalable_parallel_FFT_for_spectral_simulations_on_a_Beowulf_cluster">Scalable parallel FFT for spectral simulations on a Beowulf cluster</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The implementation and performance of the multidimensional Fast Fourier Transform (FFT) on a distributed memory Beowulf cluster is examined. We focus on the three-dimensional (3D) real transform, an essential computational component of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_52054343" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The implementation and performance of the multidimensional Fast Fourier Transform (FFT) on a distributed memory Beowulf cluster is examined. We focus on the three-dimensional (3D) real transform, an essential computational component of Galerkin and pseudospectral codes. The approach studied is a 1D domain decomposition algorithm that relies on communication-intensive transpose operation involving P processors. Communication is based upon the standard portable message passing interface (MPI). We show that 1=P scaling for execution time at ®xed problem size N 3 (i.e., linear speedup) can be obtained provided that (1) the transpose algorithm is optimized for simultaneous block communication by all processors; and (2) communication is arranged for non-overlapping pairwise communication between processors, thus eliminating blocking when standard fast ethernet interconnects are employed. This method provides the basis for implementation of scalable and ecient spectral method computations of hydrodynamic and magneto-hydrodynamic turbulence on Beowulf clusters assembled from standard commodity components. An example is presented using a 3D passive scalar code.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/52054343" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0c2f4fa1e9f437f0738dbe723d0de9a6" rel="nofollow" data-download="{"attachment_id":69495562,"asset_id":52054343,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/69495562/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="26181745" href="https://sustech-cn.academia.edu/LianPingWang">Lian-Ping Wang</a><script data-card-contents-for-user="26181745" type="text/json">{"id":26181745,"first_name":"Lian-Ping","last_name":"Wang","domain_name":"sustech-cn","page_name":"LianPingWang","display_name":"Lian-Ping Wang","profile_url":"https://sustech-cn.academia.edu/LianPingWang?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_52054343 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="52054343"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 52054343, container: ".js-paper-rank-work_52054343", }); });</script></li><li class="js-percentile-work_52054343 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 52054343; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_52054343"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_52054343 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="52054343"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 52054343; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=52054343]").text(description); $(".js-view-count-work_52054343").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_52054343").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="52054343"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="237" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a>, <script data-card-contents-for-ri="237" type="text/json">{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="440" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="442" rel="nofollow" href="https://www.academia.edu/Documents/in/Parallel_Computing">Parallel Computing</a>, <script data-card-contents-for-ri="442" type="text/json">{"id":442,"name":"Parallel Computing","url":"https://www.academia.edu/Documents/in/Parallel_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a><script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=52054343]'), work: {"id":52054343,"title":"Scalable parallel FFT for spectral simulations on a Beowulf cluster","created_at":"2021-09-12T18:47:04.972-07:00","url":"https://www.academia.edu/52054343/Scalable_parallel_FFT_for_spectral_simulations_on_a_Beowulf_cluster?f_ri=14118","dom_id":"work_52054343","summary":"The implementation and performance of the multidimensional Fast Fourier Transform (FFT) on a distributed memory Beowulf cluster is examined. We focus on the three-dimensional (3D) real transform, an essential computational component of Galerkin and pseudospectral codes. The approach studied is a 1D domain decomposition algorithm that relies on communication-intensive transpose operation involving P processors. Communication is based upon the standard portable message passing interface (MPI). We show that 1=P scaling for execution time at ®xed problem size N 3 (i.e., linear speedup) can be obtained provided that (1) the transpose algorithm is optimized for simultaneous block communication by all processors; and (2) communication is arranged for non-overlapping pairwise communication between processors, thus eliminating blocking when standard fast ethernet interconnects are employed. This method provides the basis for implementation of scalable and ecient spectral method computations of hydrodynamic and magneto-hydrodynamic turbulence on Beowulf clusters assembled from standard commodity components. An example is presented using a 3D passive scalar code.","downloadable_attachments":[{"id":69495562,"asset_id":52054343,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":26181745,"first_name":"Lian-Ping","last_name":"Wang","domain_name":"sustech-cn","page_name":"LianPingWang","display_name":"Lian-Ping Wang","profile_url":"https://sustech-cn.academia.edu/LianPingWang?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=14118","nofollow":true},{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":442,"name":"Parallel Computing","url":"https://www.academia.edu/Documents/in/Parallel_Computing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":146593,"name":"Spectral method","url":"https://www.academia.edu/Documents/in/Spectral_method?f_ri=14118"},{"id":243826,"name":"Message Passing","url":"https://www.academia.edu/Documents/in/Message_Passing?f_ri=14118"},{"id":377043,"name":"Scalability","url":"https://www.academia.edu/Documents/in/Scalability?f_ri=14118"},{"id":504035,"name":"Three Dimensional","url":"https://www.academia.edu/Documents/in/Three_Dimensional?f_ri=14118"},{"id":588226,"name":"Fast Fourier Transform","url":"https://www.academia.edu/Documents/in/Fast_Fourier_Transform?f_ri=14118"},{"id":733999,"name":"Message Passing Interface","url":"https://www.academia.edu/Documents/in/Message_Passing_Interface?f_ri=14118"},{"id":980307,"name":"Beowulf Cluster","url":"https://www.academia.edu/Documents/in/Beowulf_Cluster?f_ri=14118"},{"id":1178992,"name":"Domain Decomposition","url":"https://www.academia.edu/Documents/in/Domain_Decomposition?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_21795824" data-work_id="21795824" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/21795824/A_domain_decomposition_parallel_processing_algorithm_for_molecular_dynamics_simulations_of_polymers">A domain decomposition parallel processing algorithm for molecular dynamics simulations of polymers</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We describe in this paper a domain decomposition molecular dynamics algorithm for use on distributed memory parallel computers which is capable of handling systems containing rigid bond constrai~itsand three-and four-body potentials as... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_21795824" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We describe in this paper a domain decomposition molecular dynamics algorithm for use on distributed memory parallel computers which is capable of handling systems containing rigid bond constrai~itsand three-and four-body potentials as well as non-bonded potentials. The algorithm has been successfully implemented on the Fujitsu 1024 processor element AP1000 machine. The performance has been compared with ai~dbenchmarked against the alternative cloning method of parallel processing ED. Brown, J.H.R. Clarke, M. Okuda and T. Yamazaki, J. Chem. Phys., 100 (1994) 1684] and results obtained using other scalar and vector machines. Two parallel versions of the SHAKE algorithm, which solves the bond length constraints problem, have been compared with regard to optimising the performance of this procedure.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/21795824" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="712cc1df77512bc95f78f37b2bb70c0b" rel="nofollow" data-download="{"attachment_id":42558477,"asset_id":21795824,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42558477/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="43012290" href="https://manchester.academia.edu/JulianClarke">Julian Clarke</a><script data-card-contents-for-user="43012290" type="text/json">{"id":43012290,"first_name":"Julian","last_name":"Clarke","domain_name":"manchester","page_name":"JulianClarke","display_name":"Julian Clarke","profile_url":"https://manchester.academia.edu/JulianClarke?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_21795824 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="21795824"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 21795824, container: ".js-paper-rank-work_21795824", }); 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The algorithm has been successfully implemented on the Fujitsu 1024 processor element AP1000 machine. The performance has been compared with ai~dbenchmarked against the alternative cloning method of parallel processing ED. Brown, J.H.R. Clarke, M. Okuda and T. Yamazaki, J. Chem. Phys., 100 (1994) 1684] and results obtained using other scalar and vector machines. Two parallel versions of the SHAKE algorithm, which solves the bond length constraints problem, have been compared with regard to optimising the performance of this procedure.","downloadable_attachments":[{"id":42558477,"asset_id":21795824,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":43012290,"first_name":"Julian","last_name":"Clarke","domain_name":"manchester","page_name":"JulianClarke","display_name":"Julian Clarke","profile_url":"https://manchester.academia.edu/JulianClarke?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":17167,"name":"Parallel Processing","url":"https://www.academia.edu/Documents/in/Parallel_Processing?f_ri=14118","nofollow":true},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=14118","nofollow":true},{"id":97733,"name":"Shared memory","url":"https://www.academia.edu/Documents/in/Shared_memory?f_ri=14118","nofollow":true},{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences?f_ri=14118"},{"id":243826,"name":"Message Passing","url":"https://www.academia.edu/Documents/in/Message_Passing?f_ri=14118"},{"id":702169,"name":"Parallel Computer","url":"https://www.academia.edu/Documents/in/Parallel_Computer?f_ri=14118"},{"id":1178992,"name":"Domain Decomposition","url":"https://www.academia.edu/Documents/in/Domain_Decomposition?f_ri=14118"},{"id":1242504,"name":"Molecular Dynamic Simulation","url":"https://www.academia.edu/Documents/in/Molecular_Dynamic_Simulation?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_30873560" data-work_id="30873560" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/30873560/Coordinated_Reactivation_of_Distributed_Memory_Traces_in_Primate_Neocortex">Coordinated Reactivation of Distributed Memory Traces in Primate Neocortex</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">The user has requested enhancement of the downloaded file.</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/30873560" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" 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href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="28235" rel="nofollow" href="https://www.academia.edu/Documents/in/Multidisciplinary">Multidisciplinary</a>, <script data-card-contents-for-ri="28235" type="text/json">{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="28576" rel="nofollow" href="https://www.academia.edu/Documents/in/Prefrontal_Cortex">Prefrontal Cortex</a><script data-card-contents-for-ri="28576" type="text/json">{"id":28576,"name":"Prefrontal Cortex","url":"https://www.academia.edu/Documents/in/Prefrontal_Cortex?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=30873560]'), work: {"id":30873560,"title":"Coordinated Reactivation of Distributed Memory Traces in Primate Neocortex","created_at":"2017-01-11T04:40:16.854-08:00","url":"https://www.academia.edu/30873560/Coordinated_Reactivation_of_Distributed_Memory_Traces_in_Primate_Neocortex?f_ri=14118","dom_id":"work_30873560","summary":"The user has requested enhancement of the downloaded file.","downloadable_attachments":[{"id":51300083,"asset_id":30873560,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32547865,"first_name":"Kari","last_name":"Hoffman","domain_name":"independent","page_name":"KariHoffman","display_name":"Kari Hoffman","profile_url":"https://independent.academia.edu/KariHoffman?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":6779,"name":"Science","url":"https://www.academia.edu/Documents/in/Science?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=14118","nofollow":true},{"id":28576,"name":"Prefrontal Cortex","url":"https://www.academia.edu/Documents/in/Prefrontal_Cortex?f_ri=14118","nofollow":true},{"id":46081,"name":"Memory Consolidation","url":"https://www.academia.edu/Documents/in/Memory_Consolidation?f_ri=14118"},{"id":46858,"name":"Memory","url":"https://www.academia.edu/Documents/in/Memory?f_ri=14118"},{"id":52176,"name":"Brain Mapping","url":"https://www.academia.edu/Documents/in/Brain_Mapping?f_ri=14118"},{"id":88325,"name":"Cues","url":"https://www.academia.edu/Documents/in/Cues?f_ri=14118"},{"id":123274,"name":"Parietal Cortex","url":"https://www.academia.edu/Documents/in/Parietal_Cortex?f_ri=14118"},{"id":153836,"name":"Motor Cortex","url":"https://www.academia.edu/Documents/in/Motor_Cortex?f_ri=14118"},{"id":193974,"name":"Neurons","url":"https://www.academia.edu/Documents/in/Neurons?f_ri=14118"},{"id":277717,"name":"Somatosensory Cortex","url":"https://www.academia.edu/Documents/in/Somatosensory_Cortex?f_ri=14118"},{"id":406036,"name":"Parietal Lobe","url":"https://www.academia.edu/Documents/in/Parietal_Lobe?f_ri=14118"},{"id":413195,"name":"Time Factors","url":"https://www.academia.edu/Documents/in/Time_Factors?f_ri=14118"},{"id":493558,"name":"Neocortex","url":"https://www.academia.edu/Documents/in/Neocortex?f_ri=14118"},{"id":573267,"name":"Macaca Mulatta","url":"https://www.academia.edu/Documents/in/Macaca_Mulatta?f_ri=14118"},{"id":955727,"name":"Action Potentials","url":"https://www.academia.edu/Documents/in/Action_Potentials?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_19095441" data-work_id="19095441" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/19095441/Phoenix_rebirth_Scalable_MapReduce_on_a_large_scale_shared_memory_system">Phoenix rebirth: Scalable MapReduce on a large-scale shared-memory system</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Dynamic runtimes can simplify parallel program- ming by automatically managing concurrency and locality without further burdening the programmer. Nevertheless, implementing such runtime systems for large-scale, shared-memory systems can... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_19095441" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Dynamic runtimes can simplify parallel program- ming by automatically managing concurrency and locality without further burdening the programmer. Nevertheless, implementing such runtime systems for large-scale, shared-memory systems can be challenging. This work optimizes Phoenix, a MapReduce runtime for shared-memory multi-cores and multiprocessors, on a quad-chip, 32-core, 256-thread UltraSPARC T2+ system with NUMA characteristics. We show how a multi-layered approach that</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/19095441" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="914f8ad63d62f32b23e2f8bf06317688" rel="nofollow" data-download="{"attachment_id":42110774,"asset_id":19095441,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42110774/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="39277855" href="https://independent.academia.edu/AnthonyRomano1">Anthony Romano</a><script data-card-contents-for-user="39277855" type="text/json">{"id":39277855,"first_name":"Anthony","last_name":"Romano","domain_name":"independent","page_name":"AnthonyRomano1","display_name":"Anthony Romano","profile_url":"https://independent.academia.edu/AnthonyRomano1?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_19095441 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="19095441"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 19095441, container: ".js-paper-rank-work_19095441", }); });</script></li><li class="js-percentile-work_19095441 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19095441; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_19095441"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_19095441 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="19095441"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19095441; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19095441]").text(description); $(".js-view-count-work_19095441").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_19095441").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="19095441"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="758278" rel="nofollow" href="https://www.academia.edu/Documents/in/Large_Scale">Large Scale</a><script data-card-contents-for-ri="758278" type="text/json">{"id":758278,"name":"Large Scale","url":"https://www.academia.edu/Documents/in/Large_Scale?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=19095441]'), work: {"id":19095441,"title":"Phoenix rebirth: Scalable MapReduce on a large-scale shared-memory system","created_at":"2015-11-27T06:26:24.328-08:00","url":"https://www.academia.edu/19095441/Phoenix_rebirth_Scalable_MapReduce_on_a_large_scale_shared_memory_system?f_ri=14118","dom_id":"work_19095441","summary":"Dynamic runtimes can simplify parallel program- ming by automatically managing concurrency and locality without further burdening the programmer. Nevertheless, implementing such runtime systems for large-scale, shared-memory systems can be challenging. This work optimizes Phoenix, a MapReduce runtime for shared-memory multi-cores and multiprocessors, on a quad-chip, 32-core, 256-thread UltraSPARC T2+ system with NUMA characteristics. We show how a multi-layered approach that","downloadable_attachments":[{"id":42110774,"asset_id":19095441,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":39277855,"first_name":"Anthony","last_name":"Romano","domain_name":"independent","page_name":"AnthonyRomano1","display_name":"Anthony Romano","profile_url":"https://independent.academia.edu/AnthonyRomano1?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":758278,"name":"Large Scale","url":"https://www.academia.edu/Documents/in/Large_Scale?f_ri=14118","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_21537057" data-work_id="21537057" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/21537057/Efficient_parallel_reduction_to_bidiagonal_form">Efficient parallel reduction to bidiagonal form</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Most methods for calculating the SVD (singular value decomposition) require to ®rst bidiagonalize the matrix. The blocked reduction of a general, dense matrix to bidiagonal form, as implemented in ScaLAPACK, does about one half of the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_21537057" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Most methods for calculating the SVD (singular value decomposition) require to ®rst bidiagonalize the matrix. The blocked reduction of a general, dense matrix to bidiagonal form, as implemented in ScaLAPACK, does about one half of the operations with BLAS3. By subdividing the reduction into two stages dense 3 banded and banded 3 bidiagonal with cubic and quadratic arithmetic costs, respectively, we are able to carry out a much higher portion of the calculations in matrix±matrix multiplications. Thus, higher performance can be expected. This paper presents and compares three parallel techniques for reducing a full matrix to banded form. (The second reduction stage is described in another paper [B. Lang, Parallel Comput. 22 (1996) 1±18]). Numerical experiments on the Intel Paragon and IBM SP/1 distributed memory parallel computers demonstrate that the two-stage reduction approach can be signi®cantly superior if only the singular values are required. Ó . This work was partially funded by Deutsche Forschungsgemeinschaft, Gesch aftszeichen Fr 755/6-1 and Fr 755/6-2. 0167-8191/99/$ ± see front matter Ó 1999 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 -8 1 9 1 ( 9 9 ) 0 0 0 4 1 -1 parallel computers [1,2,4] and to novel accuracy issues, do most of the work on a full or triangular matrix.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/21537057" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ce6f324ed9cb3982d4442e0fa2a21b6a" rel="nofollow" data-download="{"attachment_id":42022381,"asset_id":21537057,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42022381/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="42646044" href="https://independent.academia.edu/LangBruno">Bruno Lang</a><script data-card-contents-for-user="42646044" type="text/json">{"id":42646044,"first_name":"Bruno","last_name":"Lang","domain_name":"independent","page_name":"LangBruno","display_name":"Bruno Lang","profile_url":"https://independent.academia.edu/LangBruno?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_21537057 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="21537057"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 21537057, container: ".js-paper-rank-work_21537057", }); 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$(".js-view-count[data-work-id=21537057]").text(description); $(".js-view-count-work_21537057").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_21537057").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="21537057"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="237" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a>, <script data-card-contents-for-ri="237" type="text/json">{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="440" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="442" rel="nofollow" href="https://www.academia.edu/Documents/in/Parallel_Computing">Parallel Computing</a>, <script data-card-contents-for-ri="442" type="text/json">{"id":442,"name":"Parallel Computing","url":"https://www.academia.edu/Documents/in/Parallel_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a><script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=21537057]'), work: {"id":21537057,"title":"Efficient parallel reduction to bidiagonal form","created_at":"2016-02-03T23:31:44.723-08:00","url":"https://www.academia.edu/21537057/Efficient_parallel_reduction_to_bidiagonal_form?f_ri=14118","dom_id":"work_21537057","summary":"Most methods for calculating the SVD (singular value decomposition) require to ®rst bidiagonalize the matrix. The blocked reduction of a general, dense matrix to bidiagonal form, as implemented in ScaLAPACK, does about one half of the operations with BLAS3. By subdividing the reduction into two stages dense 3 banded and banded 3 bidiagonal with cubic and quadratic arithmetic costs, respectively, we are able to carry out a much higher portion of the calculations in matrix±matrix multiplications. Thus, higher performance can be expected. This paper presents and compares three parallel techniques for reducing a full matrix to banded form. (The second reduction stage is described in another paper [B. Lang, Parallel Comput. 22 (1996) 1±18]). Numerical experiments on the Intel Paragon and IBM SP/1 distributed memory parallel computers demonstrate that the two-stage reduction approach can be signi®cantly superior if only the singular values are required. Ó . This work was partially funded by Deutsche Forschungsgemeinschaft, Gesch aftszeichen Fr 755/6-1 and Fr 755/6-2. 0167-8191/99/$ ± see front matter Ó 1999 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 -8 1 9 1 ( 9 9 ) 0 0 0 4 1 -1 parallel computers [1,2,4] and to novel accuracy issues, do most of the work on a full or triangular matrix.","downloadable_attachments":[{"id":42022381,"asset_id":21537057,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":42646044,"first_name":"Bruno","last_name":"Lang","domain_name":"independent","page_name":"LangBruno","display_name":"Bruno Lang","profile_url":"https://independent.academia.edu/LangBruno?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=14118","nofollow":true},{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":442,"name":"Parallel Computing","url":"https://www.academia.edu/Documents/in/Parallel_Computing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":29972,"name":"Linear Algebra","url":"https://www.academia.edu/Documents/in/Linear_Algebra?f_ri=14118"},{"id":85880,"name":"Singular value decomposition","url":"https://www.academia.edu/Documents/in/Singular_value_decomposition?f_ri=14118"},{"id":346249,"name":"Matrix Multiplication","url":"https://www.academia.edu/Documents/in/Matrix_Multiplication?f_ri=14118"},{"id":702169,"name":"Parallel Computer","url":"https://www.academia.edu/Documents/in/Parallel_Computer?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12915338" data-work_id="12915338" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/12915338/Compressing_distributed_text_in_parallel_with_s_c_dense_codes">Compressing distributed text in parallel with (s, c)-dense codes</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Systems able to cope with very large text collections are making intensive use of distributed memory parallel computing platforms such as Clusters of PCs. This is particularly evident in Web Search Engines which must resort to parallelism... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_12915338" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Systems able to cope with very large text collections are making intensive use of distributed memory parallel computing platforms such as Clusters of PCs. This is particularly evident in Web Search Engines which must resort to parallelism in order to deal efficiently with both high rates of queries per unit time and high space requirements in the form of large numbers of small documents stored in secondary memory. Those documents can be stored in compressed format to reduce memory space and communication time. This paper proposes a parallel algorithm for compressing text in such a distributed memory environment. We show efficient performance against the usual-practice alternative of compressing the whole text on a single machine.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12915338" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="78c2bf38696faa34b2f4f3d10c0e56d0" rel="nofollow" data-download="{"attachment_id":45848881,"asset_id":12915338,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/45848881/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32083745" href="https://independent.academia.edu/Mar%C3%ADnMauricio">Mauricio Marín</a><script data-card-contents-for-user="32083745" type="text/json">{"id":32083745,"first_name":"Mauricio","last_name":"Marín","domain_name":"independent","page_name":"MarínMauricio","display_name":"Mauricio Marín","profile_url":"https://independent.academia.edu/Mar%C3%ADnMauricio?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_12915338 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="12915338"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 12915338, container: ".js-paper-rank-work_12915338", }); 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$(".js-view-count[data-work-id=12915338]").text(description); $(".js-view-count-work_12915338").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_12915338").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="12915338"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="348986" rel="nofollow" href="https://www.academia.edu/Documents/in/Parallel_Algorithm">Parallel Algorithm</a>, <script data-card-contents-for-ri="348986" type="text/json">{"id":348986,"name":"Parallel Algorithm","url":"https://www.academia.edu/Documents/in/Parallel_Algorithm?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="500218" rel="nofollow" href="https://www.academia.edu/Documents/in/Web_Search_Engine">Web Search Engine</a>, <script data-card-contents-for-ri="500218" type="text/json">{"id":500218,"name":"Web Search Engine","url":"https://www.academia.edu/Documents/in/Web_Search_Engine?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2217686" rel="nofollow" href="https://www.academia.edu/Documents/in/Single_Machine">Single Machine</a><script data-card-contents-for-ri="2217686" type="text/json">{"id":2217686,"name":"Single Machine","url":"https://www.academia.edu/Documents/in/Single_Machine?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=12915338]'), work: {"id":12915338,"title":"Compressing distributed text in parallel with (s, c)-dense codes","created_at":"2015-06-10T21:05:37.787-07:00","url":"https://www.academia.edu/12915338/Compressing_distributed_text_in_parallel_with_s_c_dense_codes?f_ri=14118","dom_id":"work_12915338","summary":"Systems able to cope with very large text collections are making intensive use of distributed memory parallel computing platforms such as Clusters of PCs. This is particularly evident in Web Search Engines which must resort to parallelism in order to deal efficiently with both high rates of queries per unit time and high space requirements in the form of large numbers of small documents stored in secondary memory. Those documents can be stored in compressed format to reduce memory space and communication time. This paper proposes a parallel algorithm for compressing text in such a distributed memory environment. We show efficient performance against the usual-practice alternative of compressing the whole text on a single machine.","downloadable_attachments":[{"id":45848881,"asset_id":12915338,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32083745,"first_name":"Mauricio","last_name":"Marín","domain_name":"independent","page_name":"MarínMauricio","display_name":"Mauricio Marín","profile_url":"https://independent.academia.edu/Mar%C3%ADnMauricio?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":348986,"name":"Parallel Algorithm","url":"https://www.academia.edu/Documents/in/Parallel_Algorithm?f_ri=14118","nofollow":true},{"id":500218,"name":"Web Search Engine","url":"https://www.academia.edu/Documents/in/Web_Search_Engine?f_ri=14118","nofollow":true},{"id":2217686,"name":"Single Machine","url":"https://www.academia.edu/Documents/in/Single_Machine?f_ri=14118","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_71719885" data-work_id="71719885" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/71719885/Experience_with_Applying_Formal_Methods_to_Protocol_Specification_and_System_Architecture">Experience with Applying Formal Methods to Protocol Specification and System Architecture</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Abstract. In the last three years or so we at Enterprise Platforms Group at Intel Corporation have been applying formal methods to various problems that arose during the process of defining platform architectures for Intel’s processor... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_71719885" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Abstract. In the last three years or so we at Enterprise Platforms Group at Intel Corporation have been applying formal methods to various problems that arose during the process of defining platform architectures for Intel’s processor families. In this paper we give an overview of some of the problems we have worked on, the results we have obtained, and the lessons we have learned. The last topic is addressed mainly from the perspective of platform architects. 1. Problems and Results Modern computer systems are highly complex distributed systems with many interacting components. Architecturally they are often organized like a computer network into multiple layers: physical layer, link layer, protocol layer, etc. Most of the problems to which we applied formal methods are the formal verification (FV) of intricate protocols in the protocol and link layers. In addition, we also found several novel uses of binary decision diagrams (BDDs) [3] that are worth mentioning. 1.1. Directory-bas...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/71719885" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="27bc630ca68db4c86abe5f7595872004" rel="nofollow" data-download="{"attachment_id":80946634,"asset_id":71719885,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/80946634/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="44228950" href="https://independent.academia.edu/PhanindraMannava">Phanindra Mannava</a><script data-card-contents-for-user="44228950" type="text/json">{"id":44228950,"first_name":"Phanindra","last_name":"Mannava","domain_name":"independent","page_name":"PhanindraMannava","display_name":"Phanindra Mannava","profile_url":"https://independent.academia.edu/PhanindraMannava?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_71719885 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="71719885"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 71719885, container: ".js-paper-rank-work_71719885", }); 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$(".js-view-count[data-work-id=71719885]").text(description); $(".js-view-count-work_71719885").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_71719885").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="71719885"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">20</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="440" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14681" rel="nofollow" href="https://www.academia.edu/Documents/in/Formal_methods">Formal methods</a><script data-card-contents-for-ri="14681" type="text/json">{"id":14681,"name":"Formal methods","url":"https://www.academia.edu/Documents/in/Formal_methods?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=71719885]'), work: {"id":71719885,"title":"Experience with Applying Formal Methods to Protocol Specification and System Architecture","created_at":"2022-02-16T19:13:41.469-08:00","url":"https://www.academia.edu/71719885/Experience_with_Applying_Formal_Methods_to_Protocol_Specification_and_System_Architecture?f_ri=14118","dom_id":"work_71719885","summary":"Abstract. In the last three years or so we at Enterprise Platforms Group at Intel Corporation have been applying formal methods to various problems that arose during the process of defining platform architectures for Intel’s processor families. In this paper we give an overview of some of the problems we have worked on, the results we have obtained, and the lessons we have learned. The last topic is addressed mainly from the perspective of platform architects. 1. Problems and Results Modern computer systems are highly complex distributed systems with many interacting components. Architecturally they are often organized like a computer network into multiple layers: physical layer, link layer, protocol layer, etc. Most of the problems to which we applied formal methods are the formal verification (FV) of intricate protocols in the protocol and link layers. In addition, we also found several novel uses of binary decision diagrams (BDDs) [3] that are worth mentioning. 1.1. Directory-bas...","downloadable_attachments":[{"id":80946634,"asset_id":71719885,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":44228950,"first_name":"Phanindra","last_name":"Mannava","domain_name":"independent","page_name":"PhanindraMannava","display_name":"Phanindra Mannava","profile_url":"https://independent.academia.edu/PhanindraMannava?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=14118","nofollow":true},{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":14681,"name":"Formal methods","url":"https://www.academia.edu/Documents/in/Formal_methods?f_ri=14118","nofollow":true},{"id":22686,"name":"Distributed System","url":"https://www.academia.edu/Documents/in/Distributed_System?f_ri=14118"},{"id":62540,"name":"Computer Network","url":"https://www.academia.edu/Documents/in/Computer_Network?f_ri=14118"},{"id":64561,"name":"Computer Software","url":"https://www.academia.edu/Documents/in/Computer_Software?f_ri=14118"},{"id":126194,"name":"Formal method","url":"https://www.academia.edu/Documents/in/Formal_method?f_ri=14118"},{"id":164701,"name":"Physical Layer","url":"https://www.academia.edu/Documents/in/Physical_Layer?f_ri=14118"},{"id":172035,"name":"Formal Verification","url":"https://www.academia.edu/Documents/in/Formal_Verification?f_ri=14118"},{"id":317745,"name":"High Speed","url":"https://www.academia.edu/Documents/in/High_Speed?f_ri=14118"},{"id":383627,"name":"Sliding Window","url":"https://www.academia.edu/Documents/in/Sliding_Window?f_ri=14118"},{"id":392429,"name":"Protocol Specification","url":"https://www.academia.edu/Documents/in/Protocol_Specification?f_ri=14118"},{"id":401137,"name":"Link Layer","url":"https://www.academia.edu/Documents/in/Link_Layer?f_ri=14118"},{"id":406370,"name":"Formal Model","url":"https://www.academia.edu/Documents/in/Formal_Model?f_ri=14118"},{"id":813981,"name":"Fault Tolerant","url":"https://www.academia.edu/Documents/in/Fault_Tolerant?f_ri=14118"},{"id":1216932,"name":"Rule Based","url":"https://www.academia.edu/Documents/in/Rule_Based?f_ri=14118"},{"id":1253694,"name":"Component Architecture","url":"https://www.academia.edu/Documents/in/Component_Architecture?f_ri=14118"},{"id":2537671,"name":"Distributed Algorithm","url":"https://www.academia.edu/Documents/in/Distributed_Algorithm?f_ri=14118"},{"id":3691899,"name":"cache coherence protocol","url":"https://www.academia.edu/Documents/in/cache_coherence_protocol?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_17526824" data-work_id="17526824" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/17526824/Highly_parallel_structured_adaptive_mesh_refinement_using_parallel_language_based_approaches">Highly parallel structured adaptive mesh refinement using parallel language-based approaches</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Adaptive Mesh Re®nement (AMR) calculations carried out on structured meshes play an exceedingly important role in several areas of science and engineering. This is so not just because AMR techniques allow us to carry out calculations very... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_17526824" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Adaptive Mesh Re®nement (AMR) calculations carried out on structured meshes play an exceedingly important role in several areas of science and engineering. This is so not just because AMR techniques allow us to carry out calculations very eciently but also because they model very precisely the multi-scale fashion in which nature itself works. Many AMR applications are also amongst the most computationally intensive calculations undertaken making it necessary to use parallel supercomputers for their solution. While class library-based approaches are being attempted for parallel AMR we point out here that recent advances in the Fortran 90/95 standard and the OpenMP standard now make it possible to carry out highly parallel AMR calculations using language-based approaches. The language-based approaches oer several advantages over library-based approaches, the two principal ones being portability across parallel platforms and the best possible utilization of Distributed Shared Memory (DSM) hardware on machines that have such hardware. They also free up the applications scientist from being constrained by the static features of a class library. The choice of Fortran also ensures maximal reuse of pre-existing Fortran 77 applications and full Fortran 77-based processing eciency on each computational node. Our implementation of the ideas presented here in the ®rst author's RIEMANN framework essentially permits any serial, uniform grid, stencil-based Fortran code to be turned into a parallel AMR code. In this paper we ®rst describe our strategy for using Fortran 90 in an object-oriented fashion. This permits AMR applications to be expressed in terms of familiar abstractions that are natural to the <a href="http://www.elsevier.com/locate/parco" rel="nofollow">www.elsevier.com/locate/parco</a> Parallel Computing 27 (2001) 37±70 (D.S. Balsara), <a href="mailto:nortonc@bryce.jpl.nasa.gov" rel="nofollow">nortonc@bryce.jpl.nasa.gov</a> (C.D. Norton). 0167-8191/01/$ -see front matter Ó 2001 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 -8 1 9 1 ( 0 0 ) 0 0 0 8 8 -0</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/17526824" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="115ae8a12ee83afaaedec759ab640bd5" rel="nofollow" data-download="{"attachment_id":39559682,"asset_id":17526824,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/39559682/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="37298543" href="https://independent.academia.edu/CharlesNorton1">Charles Norton</a><script data-card-contents-for-user="37298543" type="text/json">{"id":37298543,"first_name":"Charles","last_name":"Norton","domain_name":"independent","page_name":"CharlesNorton1","display_name":"Charles Norton","profile_url":"https://independent.academia.edu/CharlesNorton1?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_17526824 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="17526824"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 17526824, container: ".js-paper-rank-work_17526824", }); 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$(".js-view-count[data-work-id=17526824]").text(description); $(".js-view-count-work_17526824").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_17526824").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="17526824"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="237" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a>, <script data-card-contents-for-ri="237" type="text/json">{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="440" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="442" rel="nofollow" href="https://www.academia.edu/Documents/in/Parallel_Computing">Parallel Computing</a>, <script data-card-contents-for-ri="442" type="text/json">{"id":442,"name":"Parallel Computing","url":"https://www.academia.edu/Documents/in/Parallel_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a><script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=17526824]'), work: {"id":17526824,"title":"Highly parallel structured adaptive mesh refinement using parallel language-based approaches","created_at":"2015-10-30T12:08:27.097-07:00","url":"https://www.academia.edu/17526824/Highly_parallel_structured_adaptive_mesh_refinement_using_parallel_language_based_approaches?f_ri=14118","dom_id":"work_17526824","summary":"Adaptive Mesh Re®nement (AMR) calculations carried out on structured meshes play an exceedingly important role in several areas of science and engineering. This is so not just because AMR techniques allow us to carry out calculations very eciently but also because they model very precisely the multi-scale fashion in which nature itself works. Many AMR applications are also amongst the most computationally intensive calculations undertaken making it necessary to use parallel supercomputers for their solution. While class library-based approaches are being attempted for parallel AMR we point out here that recent advances in the Fortran 90/95 standard and the OpenMP standard now make it possible to carry out highly parallel AMR calculations using language-based approaches. The language-based approaches oer several advantages over library-based approaches, the two principal ones being portability across parallel platforms and the best possible utilization of Distributed Shared Memory (DSM) hardware on machines that have such hardware. They also free up the applications scientist from being constrained by the static features of a class library. The choice of Fortran also ensures maximal reuse of pre-existing Fortran 77 applications and full Fortran 77-based processing eciency on each computational node. Our implementation of the ideas presented here in the ®rst author's RIEMANN framework essentially permits any serial, uniform grid, stencil-based Fortran code to be turned into a parallel AMR code. In this paper we ®rst describe our strategy for using Fortran 90 in an object-oriented fashion. This permits AMR applications to be expressed in terms of familiar abstractions that are natural to the www.elsevier.com/locate/parco Parallel Computing 27 (2001) 37±70 (D.S. Balsara), nortonc@bryce.jpl.nasa.gov (C.D. Norton). 0167-8191/01/$ -see front matter Ó 2001 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 -8 1 9 1 ( 0 0 ) 0 0 0 8 8 -0","downloadable_attachments":[{"id":39559682,"asset_id":17526824,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":37298543,"first_name":"Charles","last_name":"Norton","domain_name":"independent","page_name":"CharlesNorton1","display_name":"Charles Norton","profile_url":"https://independent.academia.edu/CharlesNorton1?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=14118","nofollow":true},{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":442,"name":"Parallel Computing","url":"https://www.academia.edu/Documents/in/Parallel_Computing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":17167,"name":"Parallel Processing","url":"https://www.academia.edu/Documents/in/Parallel_Processing?f_ri=14118"},{"id":70448,"name":"Fortran","url":"https://www.academia.edu/Documents/in/Fortran?f_ri=14118"},{"id":116313,"name":"Automatic Parallelization","url":"https://www.academia.edu/Documents/in/Automatic_Parallelization?f_ri=14118"},{"id":243828,"name":"Adaptive Mesh Refinement","url":"https://www.academia.edu/Documents/in/Adaptive_Mesh_Refinement?f_ri=14118"},{"id":394477,"name":"Time Dependent","url":"https://www.academia.edu/Documents/in/Time_Dependent?f_ri=14118"},{"id":497302,"name":"Finite Difference Method","url":"https://www.academia.edu/Documents/in/Finite_Difference_Method?f_ri=14118"},{"id":545424,"name":"Load Balance","url":"https://www.academia.edu/Documents/in/Load_Balance?f_ri=14118"},{"id":1121048,"name":"Object Oriented","url":"https://www.academia.edu/Documents/in/Object_Oriented?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_48957772" data-work_id="48957772" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/48957772/A_local_corrections_algorithm_for_solving_Poisson_s_equation_in_three_dimensions">A local corrections algorithm for solving Poisson’s equation in three dimensions</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We present a second-order accurate algorithm for solving the free-space Poisson's equation on a locally-refined nested grid hierarchy in three dimensions. Our approach is based on linear superposition of local convolutions of localized... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48957772" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We present a second-order accurate algorithm for solving the free-space Poisson's equation on a locally-refined nested grid hierarchy in three dimensions. Our approach is based on linear superposition of local convolutions of localized charge distributions, with the nonlocal coupling represented on coarser grids. The representation of the nonlocal coupling on the local solutions is based on Anderson's Method of Local Corrections and does not require iteration between different resolutions. A distributed-memory parallel implementation of this method is observed to have a computational cost per grid point less than three times that of a standard FFT-based method on a uniform grid of the same resolution, and scales well up to 1024 processors.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48957772" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ceb1a31bb7499d03a88216abda0348c6" rel="nofollow" data-download="{"attachment_id":67353269,"asset_id":48957772,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67353269/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="43172677" href="https://independent.academia.edu/PhillipColella">Phillip Colella</a><script data-card-contents-for-user="43172677" type="text/json">{"id":43172677,"first_name":"Phillip","last_name":"Colella","domain_name":"independent","page_name":"PhillipColella","display_name":"Phillip Colella","profile_url":"https://independent.academia.edu/PhillipColella?f_ri=14118","photo":"https://0.academia-photos.com/43172677/139678418/129157755/s65_phillip.colella.png"}</script></span></span></li><li class="js-paper-rank-work_48957772 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48957772"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48957772, container: ".js-paper-rank-work_48957772", }); 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$(".js-view-count[data-work-id=48957772]").text(description); $(".js-view-count-work_48957772").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_48957772").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="48957772"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="305" rel="nofollow" href="https://www.academia.edu/Documents/in/Applied_Mathematics">Applied Mathematics</a>, <script data-card-contents-for-ri="305" type="text/json">{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="243828" rel="nofollow" href="https://www.academia.edu/Documents/in/Adaptive_Mesh_Refinement">Adaptive Mesh Refinement</a>, <script data-card-contents-for-ri="243828" type="text/json">{"id":243828,"name":"Adaptive Mesh Refinement","url":"https://www.academia.edu/Documents/in/Adaptive_Mesh_Refinement?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="347272" rel="nofollow" href="https://www.academia.edu/Documents/in/Second_Order">Second Order</a><script data-card-contents-for-ri="347272" type="text/json">{"id":347272,"name":"Second Order","url":"https://www.academia.edu/Documents/in/Second_Order?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=48957772]'), work: {"id":48957772,"title":"A local corrections algorithm for solving Poisson’s equation in three dimensions","created_at":"2021-05-17T16:44:38.016-07:00","url":"https://www.academia.edu/48957772/A_local_corrections_algorithm_for_solving_Poisson_s_equation_in_three_dimensions?f_ri=14118","dom_id":"work_48957772","summary":"We present a second-order accurate algorithm for solving the free-space Poisson's equation on a locally-refined nested grid hierarchy in three dimensions. Our approach is based on linear superposition of local convolutions of localized charge distributions, with the nonlocal coupling represented on coarser grids. The representation of the nonlocal coupling on the local solutions is based on Anderson's Method of Local Corrections and does not require iteration between different resolutions. A distributed-memory parallel implementation of this method is observed to have a computational cost per grid point less than three times that of a standard FFT-based method on a uniform grid of the same resolution, and scales well up to 1024 processors.","downloadable_attachments":[{"id":67353269,"asset_id":48957772,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":43172677,"first_name":"Phillip","last_name":"Colella","domain_name":"independent","page_name":"PhillipColella","display_name":"Phillip Colella","profile_url":"https://independent.academia.edu/PhillipColella?f_ri=14118","photo":"https://0.academia-photos.com/43172677/139678418/129157755/s65_phillip.colella.png"}],"research_interests":[{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":243828,"name":"Adaptive Mesh Refinement","url":"https://www.academia.edu/Documents/in/Adaptive_Mesh_Refinement?f_ri=14118","nofollow":true},{"id":347272,"name":"Second Order","url":"https://www.academia.edu/Documents/in/Second_Order?f_ri=14118","nofollow":true},{"id":497302,"name":"Finite Difference Method","url":"https://www.academia.edu/Documents/in/Finite_Difference_Method?f_ri=14118"},{"id":809731,"name":"Poisson Equation","url":"https://www.academia.edu/Documents/in/Poisson_Equation?f_ri=14118"},{"id":1208729,"name":"Charge Distribution","url":"https://www.academia.edu/Documents/in/Charge_Distribution?f_ri=14118"},{"id":2274108,"name":"Lawrence Berkeley Laboratory","url":"https://www.academia.edu/Documents/in/Lawrence_Berkeley_Laboratory?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_30075955" data-work_id="30075955" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/30075955/Mayfly_A_general_purpose_scalable_parallel_processing_architecture">Mayfly: A general-purpose, scalable, parallel processing architecture</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The Mayfly is a scalable general-purpose parallel processing system being designed at HP Laboratories, in collaboration with colleagues at the University of Utah. The system is intended to efficiently support parallel variants of modern... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_30075955" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The Mayfly is a scalable general-purpose parallel processing system being designed at HP Laboratories, in collaboration with colleagues at the University of Utah. The system is intended to efficiently support parallel variants of modern programming languages such as Lisp, Prolog, and Object Oriented Programming models. These languages impose a common requirement on the hardware platform to support dynamic system needs such as runtime type checking and dynamic storage management. The main programming language for the Mayfly is a concurrent dialect of Scheme. The system is based on a distributed-memory model, and communication between processing elements is supported by message passing. The initial prototype of Mayfly will consist of 19 identical processing elements interconnected in a hexagonal mesh structure. In order to achieve the goal of scalable performance, each processing element is a parallel processor as well, which permits the application code, runtime operating system, and communication to all run in parallel. A 7 processing element subset of the prototype is presently operational. This paper describes the hardware architecture after a brief background synopsis of the software system structure.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/30075955" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9ef67500f1ecae720bc1b6db2eda8297" rel="nofollow" data-download="{"attachment_id":50524946,"asset_id":30075955,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50524946/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="57182406" href="https://independent.academia.edu/LeighStoller">Leigh Stoller</a><script data-card-contents-for-user="57182406" type="text/json">{"id":57182406,"first_name":"Leigh","last_name":"Stoller","domain_name":"independent","page_name":"LeighStoller","display_name":"Leigh Stoller","profile_url":"https://independent.academia.edu/LeighStoller?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_30075955 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="30075955"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 30075955, container: ".js-paper-rank-work_30075955", }); 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$(".js-view-count[data-work-id=30075955]").text(description); $(".js-view-count-work_30075955").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_30075955").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="30075955"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">11</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="237" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a>, <script data-card-contents-for-ri="237" type="text/json">{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="453" rel="nofollow" href="https://www.academia.edu/Documents/in/Object_Oriented_Programming">Object Oriented Programming</a>, <script data-card-contents-for-ri="453" type="text/json">{"id":453,"name":"Object Oriented Programming","url":"https://www.academia.edu/Documents/in/Object_Oriented_Programming?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="17167" rel="nofollow" href="https://www.academia.edu/Documents/in/Parallel_Processing">Parallel Processing</a><script data-card-contents-for-ri="17167" type="text/json">{"id":17167,"name":"Parallel Processing","url":"https://www.academia.edu/Documents/in/Parallel_Processing?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=30075955]'), work: {"id":30075955,"title":"Mayfly: A general-purpose, scalable, parallel processing architecture","created_at":"2016-11-24T10:49:44.132-08:00","url":"https://www.academia.edu/30075955/Mayfly_A_general_purpose_scalable_parallel_processing_architecture?f_ri=14118","dom_id":"work_30075955","summary":"The Mayfly is a scalable general-purpose parallel processing system being designed at HP Laboratories, in collaboration with colleagues at the University of Utah. The system is intended to efficiently support parallel variants of modern programming languages such as Lisp, Prolog, and Object Oriented Programming models. These languages impose a common requirement on the hardware platform to support dynamic system needs such as runtime type checking and dynamic storage management. The main programming language for the Mayfly is a concurrent dialect of Scheme. The system is based on a distributed-memory model, and communication between processing elements is supported by message passing. The initial prototype of Mayfly will consist of 19 identical processing elements interconnected in a hexagonal mesh structure. In order to achieve the goal of scalable performance, each processing element is a parallel processor as well, which permits the application code, runtime operating system, and communication to all run in parallel. A 7 processing element subset of the prototype is presently operational. This paper describes the hardware architecture after a brief background synopsis of the software system structure.","downloadable_attachments":[{"id":50524946,"asset_id":30075955,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":57182406,"first_name":"Leigh","last_name":"Stoller","domain_name":"independent","page_name":"LeighStoller","display_name":"Leigh Stoller","profile_url":"https://independent.academia.edu/LeighStoller?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=14118","nofollow":true},{"id":453,"name":"Object Oriented Programming","url":"https://www.academia.edu/Documents/in/Object_Oriented_Programming?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":17167,"name":"Parallel Processing","url":"https://www.academia.edu/Documents/in/Parallel_Processing?f_ri=14118","nofollow":true},{"id":44244,"name":"OPERATING SYSTEM","url":"https://www.academia.edu/Documents/in/OPERATING_SYSTEM?f_ri=14118"},{"id":243826,"name":"Message Passing","url":"https://www.academia.edu/Documents/in/Message_Passing?f_ri=14118"},{"id":567681,"name":"Software Systems","url":"https://www.academia.edu/Documents/in/Software_Systems?f_ri=14118"},{"id":588026,"name":"Data Storage Management in Cloud","url":"https://www.academia.edu/Documents/in/Data_Storage_Management_in_Cloud?f_ri=14118"},{"id":1489478,"name":"Programming language","url":"https://www.academia.edu/Documents/in/Programming_language?f_ri=14118"},{"id":1809959,"name":"Hardware architecture","url":"https://www.academia.edu/Documents/in/Hardware_architecture?f_ri=14118"},{"id":2150506,"name":"Processing Element","url":"https://www.academia.edu/Documents/in/Processing_Element?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_14379684" data-work_id="14379684" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/14379684/Parallel_Arnoldi_eigensolvers_with_enhanced_scalability_via_global_communications_rearrangement">Parallel Arnoldi eigensolvers with enhanced scalability via global communications rearrangement</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper presents several new variants of the single-vector Arnoldi algorithm for computing approximations to eigenvalues and eigenvectors of a non-symmetric matrix. The context of this work is the efficient implementation of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_14379684" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper presents several new variants of the single-vector Arnoldi algorithm for computing approximations to eigenvalues and eigenvectors of a non-symmetric matrix. The context of this work is the efficient implementation of industrialstrength, parallel, sparse eigensolvers, in which robustness is of paramount importance, as well as efficiency. For this reason, Arnoldi variants that employ Gram-Schmidt with iterative reorthogonalization are considered. The proposed algorithms aim at improving the scalability when running in massively parallel platforms with many processors. The main goal is to reduce the performance penalty induced by global communications required in vector inner products and norms. In the proposed algorithms, this is achieved by reorganizing the stages that involve these operations, particularly the orthogonalization and normalization of vectors, in such a way that several global communications are grouped together while guaranteeing that the numerical stability of the process is maintained. The numerical properties of the new algorithms are assessed by means of a large set of test matrices. Also, scalability analyses show a significant improvement in parallel performance.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/14379684" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="35ba02e6a74c1fce538cf0bbac2b6d30" rel="nofollow" data-download="{"attachment_id":44263523,"asset_id":14379684,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/44263523/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33322665" href="https://colpos.academia.edu/VicenteHernandez">Vicente Hernandez</a><script data-card-contents-for-user="33322665" type="text/json">{"id":33322665,"first_name":"Vicente","last_name":"Hernandez","domain_name":"colpos","page_name":"VicenteHernandez","display_name":"Vicente Hernandez","profile_url":"https://colpos.academia.edu/VicenteHernandez?f_ri=14118","photo":"https://0.academia-photos.com/33322665/158191796/147836327/s65_vicente.hernandez.png"}</script></span></span></li><li class="js-paper-rank-work_14379684 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="14379684"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 14379684, container: ".js-paper-rank-work_14379684", }); 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$(".js-view-count[data-work-id=14379684]").text(description); $(".js-view-count-work_14379684").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_14379684").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="14379684"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="237" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a>, <script data-card-contents-for-ri="237" type="text/json">{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="440" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="442" rel="nofollow" href="https://www.academia.edu/Documents/in/Parallel_Computing">Parallel Computing</a>, <script data-card-contents-for-ri="442" type="text/json">{"id":442,"name":"Parallel Computing","url":"https://www.academia.edu/Documents/in/Parallel_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a><script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=14379684]'), work: {"id":14379684,"title":"Parallel Arnoldi eigensolvers with enhanced scalability via global communications rearrangement","created_at":"2015-07-24T23:34:08.550-07:00","url":"https://www.academia.edu/14379684/Parallel_Arnoldi_eigensolvers_with_enhanced_scalability_via_global_communications_rearrangement?f_ri=14118","dom_id":"work_14379684","summary":"This paper presents several new variants of the single-vector Arnoldi algorithm for computing approximations to eigenvalues and eigenvectors of a non-symmetric matrix. The context of this work is the efficient implementation of industrialstrength, parallel, sparse eigensolvers, in which robustness is of paramount importance, as well as efficiency. For this reason, Arnoldi variants that employ Gram-Schmidt with iterative reorthogonalization are considered. The proposed algorithms aim at improving the scalability when running in massively parallel platforms with many processors. The main goal is to reduce the performance penalty induced by global communications required in vector inner products and norms. In the proposed algorithms, this is achieved by reorganizing the stages that involve these operations, particularly the orthogonalization and normalization of vectors, in such a way that several global communications are grouped together while guaranteeing that the numerical stability of the process is maintained. The numerical properties of the new algorithms are assessed by means of a large set of test matrices. Also, scalability analyses show a significant improvement in parallel performance.","downloadable_attachments":[{"id":44263523,"asset_id":14379684,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":33322665,"first_name":"Vicente","last_name":"Hernandez","domain_name":"colpos","page_name":"VicenteHernandez","display_name":"Vicente Hernandez","profile_url":"https://colpos.academia.edu/VicenteHernandez?f_ri=14118","photo":"https://0.academia-photos.com/33322665/158191796/147836327/s65_vicente.hernandez.png"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=14118","nofollow":true},{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":442,"name":"Parallel Computing","url":"https://www.academia.edu/Documents/in/Parallel_Computing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":606723,"name":"Numerical Stability","url":"https://www.academia.edu/Documents/in/Numerical_Stability?f_ri=14118"},{"id":891011,"name":"Eigenvalues and Eigenvectors","url":"https://www.academia.edu/Documents/in/Eigenvalues_and_Eigenvectors?f_ri=14118"},{"id":1978689,"name":"Symmetric Matrix","url":"https://www.academia.edu/Documents/in/Symmetric_Matrix?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_16886004" data-work_id="16886004" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/16886004/Distributed_shared_memory_a_survey_of_issues_and_algorithms">Distributed shared memory: a survey of issues and algorithms</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A s we slowly approach the physical limits of processor and memory speed, it is becoming more attractive to use multiprocessors to increase comput-ing power. Two kinds of parallel processors have become popular: tightly coupled... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_16886004" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A s we slowly approach the physical limits of processor and memory speed, it is becoming more attractive to use multiprocessors to increase comput-ing power. Two kinds of parallel processors have become popular: tightly coupled shared-memory multiprocessors and distributed-...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/16886004" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="aed46da26902b886c2a7d84ce8f08eab" rel="nofollow" data-download="{"attachment_id":42379407,"asset_id":16886004,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42379407/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="36365445" href="https://independent.academia.edu/BNitzberg">B. Nitzberg</a><script data-card-contents-for-user="36365445" type="text/json">{"id":36365445,"first_name":"B.","last_name":"Nitzberg","domain_name":"independent","page_name":"BNitzberg","display_name":"B. 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Two kinds of parallel processors have become popular: tightly coupled shared-memory multiprocessors and distributed-...","downloadable_attachments":[{"id":42379407,"asset_id":16886004,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":36365445,"first_name":"B.","last_name":"Nitzberg","domain_name":"independent","page_name":"BNitzberg","display_name":"B. Nitzberg","profile_url":"https://independent.academia.edu/BNitzberg?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":12594,"name":"Memory Management","url":"https://www.academia.edu/Documents/in/Memory_Management?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":1198057,"name":"Computer","url":"https://www.academia.edu/Documents/in/Computer?f_ri=14118","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_14908173" data-work_id="14908173" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/14908173/Munin_distributed_shared_memory_based_on_type_specific_memory_coherence">Munin: distributed shared memory based on type-specific memory coherence</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We are developing Munin, a system that allows programs written for shared memory multiprocessors to be executed e ciently on distributed memory machines. Munin attempts to overcome the architectural limitations of shared memory machines,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_14908173" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We are developing Munin, a system that allows programs written for shared memory multiprocessors to be executed e ciently on distributed memory machines. Munin attempts to overcome the architectural limitations of shared memory machines, while maintaining their advantages in terms of ease of programming. Our system is unique in its use of loosely coherent memory, based on the partial order speci ed by a shared memory parallel program, and in its use of type-speci c memory coherence. Instead of a single memory coherence mechanism for all shared data objects, Munin employs several di erent mechanisms, each appropriate for a di erent class of shared data object. These type-speci c mechanisms are part of a runtime system that accepts hints from the user or the compiler to determine the coherence mechanism to be used for each object. This paper focuses on the design and use of Munin's memory coherence mechanisms, and compares our approach to previous work in this area. y In Norse mythology, Munin (Memory) was one of two ravens perched on Odin's shoulder. Each day, Munin would y across the world and bring back to Odin knowledge of man's memory. Thus, the raven Munin might be considered the world's rst distributed shared memory mechanism.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/14908173" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6e904e2281dc2935f1a85cc4a5542ff0" rel="nofollow" data-download="{"attachment_id":43787681,"asset_id":14908173,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43787681/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33897006" href="https://independent.academia.edu/johncarter101">john carter</a><script data-card-contents-for-user="33897006" type="text/json">{"id":33897006,"first_name":"john","last_name":"carter","domain_name":"independent","page_name":"johncarter101","display_name":"john carter","profile_url":"https://independent.academia.edu/johncarter101?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_14908173 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="14908173"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 14908173, container: ".js-paper-rank-work_14908173", }); 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Munin attempts to overcome the architectural limitations of shared memory machines, while maintaining their advantages in terms of ease of programming. Our system is unique in its use of loosely coherent memory, based on the partial order speci ed by a shared memory parallel program, and in its use of type-speci c memory coherence. Instead of a single memory coherence mechanism for all shared data objects, Munin employs several di erent mechanisms, each appropriate for a di erent class of shared data object. These type-speci c mechanisms are part of a runtime system that accepts hints from the user or the compiler to determine the coherence mechanism to be used for each object. This paper focuses on the design and use of Munin's memory coherence mechanisms, and compares our approach to previous work in this area. y In Norse mythology, Munin (Memory) was one of two ravens perched on Odin's shoulder. Each day, Munin would y across the world and bring back to Odin knowledge of man's memory. Thus, the raven Munin might be considered the world's rst distributed shared memory mechanism.","downloadable_attachments":[{"id":43787681,"asset_id":14908173,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":33897006,"first_name":"john","last_name":"carter","domain_name":"independent","page_name":"johncarter101","display_name":"john carter","profile_url":"https://independent.academia.edu/johncarter101?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":97733,"name":"Shared memory","url":"https://www.academia.edu/Documents/in/Shared_memory?f_ri=14118","nofollow":true},{"id":980672,"name":"Perch","url":"https://www.academia.edu/Documents/in/Perch?f_ri=14118","nofollow":true},{"id":1983387,"name":"Partial Order","url":"https://www.academia.edu/Documents/in/Partial_Order?f_ri=14118","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_685759" data-work_id="685759" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/685759/Matrix_algorithms_on_a_hypercube_I_Matrix_multiplication_1">Matrix algorithms on a hypercube I: Matrix multiplication* 1</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We discuss algorithms for matrix multiplication on a concurrent processor containing a two-dimensional mesh or richer topology. We present detailed performance measurements on hypercubes with 4, 1,6, and 64 nodes, and analyze them in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_685759" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We discuss algorithms for matrix multiplication on a concurrent processor containing a two-dimensional mesh or richer topology. We present detailed performance measurements on hypercubes with 4, 1,6, and 64 nodes, and analyze them in ter:'as of communication overhead and load balancing. We show that the decomposition into square subblocks is optimal C code implementing the algorithms is available.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/685759" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0f411b3cd6cd3de06fe7c94bff488d8f" rel="nofollow" data-download="{"attachment_id":51292821,"asset_id":685759,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/51292821/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="356255" href="https://indiana.academia.edu/GeoffreyFox">Geoffrey Fox</a><script data-card-contents-for-user="356255" type="text/json">{"id":356255,"first_name":"Geoffrey","last_name":"Fox","domain_name":"indiana","page_name":"GeoffreyFox","display_name":"Geoffrey Fox","profile_url":"https://indiana.academia.edu/GeoffreyFox?f_ri=14118","photo":"https://0.academia-photos.com/356255/9312926/10380188/s65_geoffrey.fox.jpg"}</script></span></span></li><li class="js-paper-rank-work_685759 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="685759"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 685759, container: ".js-paper-rank-work_685759", }); 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$(".js-view-count[data-work-id=685759]").text(description); $(".js-view-count-work_685759").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_685759").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="685759"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">11</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="237" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a>, <script data-card-contents-for-ri="237" type="text/json">{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="440" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="442" rel="nofollow" href="https://www.academia.edu/Documents/in/Parallel_Computing">Parallel Computing</a>, <script data-card-contents-for-ri="442" type="text/json">{"id":442,"name":"Parallel Computing","url":"https://www.academia.edu/Documents/in/Parallel_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a><script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=685759]'), work: {"id":685759,"title":"Matrix algorithms on a hypercube I: Matrix multiplication* 1","created_at":"2011-06-20T09:05:37.863-07:00","url":"https://www.academia.edu/685759/Matrix_algorithms_on_a_hypercube_I_Matrix_multiplication_1?f_ri=14118","dom_id":"work_685759","summary":"We discuss algorithms for matrix multiplication on a concurrent processor containing a two-dimensional mesh or richer topology. We present detailed performance measurements on hypercubes with 4, 1,6, and 64 nodes, and analyze them in ter:'as of communication overhead and load balancing. We show that the decomposition into square subblocks is optimal C code implementing the algorithms is available.","downloadable_attachments":[{"id":51292821,"asset_id":685759,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":356255,"first_name":"Geoffrey","last_name":"Fox","domain_name":"indiana","page_name":"GeoffreyFox","display_name":"Geoffrey Fox","profile_url":"https://indiana.academia.edu/GeoffreyFox?f_ri=14118","photo":"https://0.academia-photos.com/356255/9312926/10380188/s65_geoffrey.fox.jpg"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=14118","nofollow":true},{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":442,"name":"Parallel Computing","url":"https://www.academia.edu/Documents/in/Parallel_Computing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":17167,"name":"Parallel Processing","url":"https://www.academia.edu/Documents/in/Parallel_Processing?f_ri=14118"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm?f_ri=14118"},{"id":46858,"name":"Memory","url":"https://www.academia.edu/Documents/in/Memory?f_ri=14118"},{"id":135038,"name":"Load Balancing","url":"https://www.academia.edu/Documents/in/Load_Balancing?f_ri=14118"},{"id":271412,"name":"HyperCube","url":"https://www.academia.edu/Documents/in/HyperCube?f_ri=14118"},{"id":346249,"name":"Matrix Multiplication","url":"https://www.academia.edu/Documents/in/Matrix_Multiplication?f_ri=14118"},{"id":552138,"name":"Adomian decomposition method","url":"https://www.academia.edu/Documents/in/Adomian_decomposition_method?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_21405234" data-work_id="21405234" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/21405234/Using_MPI_with_C_and_the_Common_Language_Infrastructure">Using MPI with C# and the Common Language Infrastructure</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We describe two different libraries for using the Message Passing Interface (MPI) with the C# programming language and the Common Language Infrastructure (CLI). The first library provides C# bindings that closely match the original MPI... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_21405234" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We describe two different libraries for using the Message Passing Interface (MPI) with the C# programming language and the Common Language Infrastructure (CLI). The first library provides C# bindings that closely match the original MPI library specification. The second library presents a fully objectoriented interface to MPI and exploits modern language features of C#. The interfaces described here use the P/Invoke feature of the CLI to dispatch to a native implementation of MPI, such as LAM/MPI or MPICH. Performance results using the Shared Source CLI demonstrate only a small performance overhead.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/21405234" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8267ea2f9ed86d0ec1edcfcea979bbbf" rel="nofollow" data-download="{"attachment_id":41855612,"asset_id":21405234,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/41855612/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="42473348" href="https://independent.academia.edu/ArchRobison">Arch D Robison</a><script data-card-contents-for-user="42473348" type="text/json">{"id":42473348,"first_name":"Arch","last_name":"Robison","domain_name":"independent","page_name":"ArchRobison","display_name":"Arch D Robison","profile_url":"https://independent.academia.edu/ArchRobison?f_ri=14118","photo":"https://gravatar.com/avatar/94b16257bce70429e8174ad94cddc2a8?s=65"}</script></span></span></li><li class="js-paper-rank-work_21405234 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="21405234"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 21405234, container: ".js-paper-rank-work_21405234", }); 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The first library provides C# bindings that closely match the original MPI library specification. The second library presents a fully objectoriented interface to MPI and exploits modern language features of C#. The interfaces described here use the P/Invoke feature of the CLI to dispatch to a native implementation of MPI, such as LAM/MPI or MPICH. Performance results using the Shared Source CLI demonstrate only a small performance overhead.","downloadable_attachments":[{"id":41855612,"asset_id":21405234,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":42473348,"first_name":"Arch","last_name":"Robison","domain_name":"independent","page_name":"ArchRobison","display_name":"Arch D Robison","profile_url":"https://independent.academia.edu/ArchRobison?f_ri=14118","photo":"https://gravatar.com/avatar/94b16257bce70429e8174ad94cddc2a8?s=65"}],"research_interests":[{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":15015,"name":"Access To Information","url":"https://www.academia.edu/Documents/in/Access_To_Information?f_ri=14118","nofollow":true},{"id":29073,"name":"Network optimization","url":"https://www.academia.edu/Documents/in/Network_optimization?f_ri=14118","nofollow":true},{"id":32373,"name":"Programming Language Design","url":"https://www.academia.edu/Documents/in/Programming_Language_Design?f_ri=14118"},{"id":53994,"name":"Data Structure","url":"https://www.academia.edu/Documents/in/Data_Structure?f_ri=14118"},{"id":64561,"name":"Computer Software","url":"https://www.academia.edu/Documents/in/Computer_Software?f_ri=14118"},{"id":110990,"name":"Virtual Machine","url":"https://www.academia.edu/Documents/in/Virtual_Machine?f_ri=14118"},{"id":133934,"name":"Levels of Abstraction","url":"https://www.academia.edu/Documents/in/Levels_of_Abstraction?f_ri=14118"},{"id":138293,"name":"Level Set","url":"https://www.academia.edu/Documents/in/Level_Set?f_ri=14118"},{"id":243826,"name":"Message Passing","url":"https://www.academia.edu/Documents/in/Message_Passing?f_ri=14118"},{"id":297691,"name":"High performance","url":"https://www.academia.edu/Documents/in/High_performance?f_ri=14118"},{"id":702169,"name":"Parallel Computer","url":"https://www.academia.edu/Documents/in/Parallel_Computer?f_ri=14118"},{"id":733999,"name":"Message Passing Interface","url":"https://www.academia.edu/Documents/in/Message_Passing_Interface?f_ri=14118"},{"id":1121048,"name":"Object Oriented","url":"https://www.academia.edu/Documents/in/Object_Oriented?f_ri=14118"},{"id":1489478,"name":"Programming language","url":"https://www.academia.edu/Documents/in/Programming_language?f_ri=14118"},{"id":1547415,"name":"Data Type","url":"https://www.academia.edu/Documents/in/Data_Type?f_ri=14118"},{"id":1834487,"name":"C Programming Language","url":"https://www.academia.edu/Documents/in/C_Programming_Language?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_23881980 coauthored" data-work_id="23881980" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/23881980/Deadlock_detection_in_MPI_programs">Deadlock detection in MPI programs</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The Message-Passing Interface (MPI) is commonly used to write parallel programs for distributed memory parallel computers. MPI-CHECK is a tool developed to aid in the debugging of MPI programs that are written in free or fixed format... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_23881980" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The Message-Passing Interface (MPI) is commonly used to write parallel programs for distributed memory parallel computers. MPI-CHECK is a tool developed to aid in the debugging of MPI programs that are written in free or fixed format Fortran 90 and Fortran 77. This paper presents the methods used in MPI-CHECK 2.0 to detect many situations where actual and potential deadlocks occur when using blocking and non-blocking point-to-point routines as well as when using collective routines.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/23881980" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f525e469a643aa870df7ba0a131cd3aa" rel="nofollow" data-download="{"attachment_id":44271384,"asset_id":23881980,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/44271384/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="46152850" href="https://independent.academia.edu/MarinaKraeva">Marina Kraeva</a><script data-card-contents-for-user="46152850" type="text/json">{"id":46152850,"first_name":"Marina","last_name":"Kraeva","domain_name":"independent","page_name":"MarinaKraeva","display_name":"Marina Kraeva","profile_url":"https://independent.academia.edu/MarinaKraeva?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-23881980">+2</span><div class="hidden js-additional-users-23881980"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/GlennLuecke">Glenn Luecke</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/JCoyle2">J. Coyle</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-23881980'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-23881980').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_23881980 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="23881980"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 23881980, container: ".js-paper-rank-work_23881980", }); });</script></li><li class="js-percentile-work_23881980 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 23881980; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_23881980"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_23881980 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="23881980"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 23881980; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=23881980]").text(description); $(".js-view-count-work_23881980").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_23881980").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="23881980"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="440" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="64561" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Software">Computer Software</a>, <script data-card-contents-for-ri="64561" type="text/json">{"id":64561,"name":"Computer Software","url":"https://www.academia.edu/Documents/in/Computer_Software?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="70448" rel="nofollow" href="https://www.academia.edu/Documents/in/Fortran">Fortran</a><script data-card-contents-for-ri="70448" type="text/json">{"id":70448,"name":"Fortran","url":"https://www.academia.edu/Documents/in/Fortran?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=23881980]'), work: {"id":23881980,"title":"Deadlock detection in MPI programs","created_at":"2016-03-31T13:14:29.561-07:00","url":"https://www.academia.edu/23881980/Deadlock_detection_in_MPI_programs?f_ri=14118","dom_id":"work_23881980","summary":"The Message-Passing Interface (MPI) is commonly used to write parallel programs for distributed memory parallel computers. MPI-CHECK is a tool developed to aid in the debugging of MPI programs that are written in free or fixed format Fortran 90 and Fortran 77. This paper presents the methods used in MPI-CHECK 2.0 to detect many situations where actual and potential deadlocks occur when using blocking and non-blocking point-to-point routines as well as when using collective routines.","downloadable_attachments":[{"id":44271384,"asset_id":23881980,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":46152850,"first_name":"Marina","last_name":"Kraeva","domain_name":"independent","page_name":"MarinaKraeva","display_name":"Marina Kraeva","profile_url":"https://independent.academia.edu/MarinaKraeva?f_ri=14118","photo":"/images/s65_no_pic.png"},{"id":46152706,"first_name":"Glenn","last_name":"Luecke","domain_name":"independent","page_name":"GlennLuecke","display_name":"Glenn Luecke","profile_url":"https://independent.academia.edu/GlennLuecke?f_ri=14118","photo":"/images/s65_no_pic.png"},{"id":46264852,"first_name":"J.","last_name":"Coyle","domain_name":"independent","page_name":"JCoyle2","display_name":"J. Coyle","profile_url":"https://independent.academia.edu/JCoyle2?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":64561,"name":"Computer Software","url":"https://www.academia.edu/Documents/in/Computer_Software?f_ri=14118","nofollow":true},{"id":70448,"name":"Fortran","url":"https://www.academia.edu/Documents/in/Fortran?f_ri=14118","nofollow":true},{"id":702169,"name":"Parallel Computer","url":"https://www.academia.edu/Documents/in/Parallel_Computer?f_ri=14118"},{"id":733999,"name":"Message Passing Interface","url":"https://www.academia.edu/Documents/in/Message_Passing_Interface?f_ri=14118"},{"id":1149159,"name":"Deadlock Detection","url":"https://www.academia.edu/Documents/in/Deadlock_Detection?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_53595777" data-work_id="53595777" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/53595777/Sequoia_Programming_the_Memory_Hierarchy">Sequoia: Programming the Memory Hierarchy</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We present Sequoia, a programming language designed to facilitate the development of memory hierarchy aware parallel programs that remain portable across modern machines featuring different memory hierarchy configurations. Sequoia... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_53595777" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We present Sequoia, a programming language designed to facilitate the development of memory hierarchy aware parallel programs that remain portable across modern machines featuring different memory hierarchy configurations. Sequoia abstractly exposes hierarchical memory in the programming model and provides language mechanisms to describe communication vertically through the machine and to localize computation to particular memory locations within it. We have implemented a complete programming system, including a compiler and runtime systems for Cell processor-based blade systems and distributed memory clusters, and demonstrate efficient performance running Sequoia programs on both of these platforms.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/53595777" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="04ac1def7632d055a22f32fa5800ad76" rel="nofollow" data-download="{"attachment_id":70368333,"asset_id":53595777,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/70368333/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="150594801" href="https://independent.academia.edu/MikeHouston7">Mike Houston</a><script data-card-contents-for-user="150594801" type="text/json">{"id":150594801,"first_name":"Mike","last_name":"Houston","domain_name":"independent","page_name":"MikeHouston7","display_name":"Mike Houston","profile_url":"https://independent.academia.edu/MikeHouston7?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_53595777 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="53595777"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 53595777, container: ".js-paper-rank-work_53595777", }); 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$(".js-view-count[data-work-id=53595777]").text(description); $(".js-view-count-work_53595777").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_53595777").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="53595777"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3703" rel="nofollow" href="https://www.academia.edu/Documents/in/Network_Security">Network Security</a>, <script data-card-contents-for-ri="3703" type="text/json">{"id":3703,"name":"Network Security","url":"https://www.academia.edu/Documents/in/Network_Security?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6078" rel="nofollow" href="https://www.academia.edu/Documents/in/Visual_Analytics">Visual Analytics</a>, <script data-card-contents-for-ri="6078" type="text/json">{"id":6078,"name":"Visual Analytics","url":"https://www.academia.edu/Documents/in/Visual_Analytics?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a><script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=53595777]'), work: {"id":53595777,"title":"Sequoia: Programming the Memory Hierarchy","created_at":"2021-09-27T20:57:35.210-07:00","url":"https://www.academia.edu/53595777/Sequoia_Programming_the_Memory_Hierarchy?f_ri=14118","dom_id":"work_53595777","summary":"We present Sequoia, a programming language designed to facilitate the development of memory hierarchy aware parallel programs that remain portable across modern machines featuring different memory hierarchy configurations. Sequoia abstractly exposes hierarchical memory in the programming model and provides language mechanisms to describe communication vertically through the machine and to localize computation to particular memory locations within it. We have implemented a complete programming system, including a compiler and runtime systems for Cell processor-based blade systems and distributed memory clusters, and demonstrate efficient performance running Sequoia programs on both of these platforms.","downloadable_attachments":[{"id":70368333,"asset_id":53595777,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":150594801,"first_name":"Mike","last_name":"Houston","domain_name":"independent","page_name":"MikeHouston7","display_name":"Mike Houston","profile_url":"https://independent.academia.edu/MikeHouston7?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=14118","nofollow":true},{"id":3703,"name":"Network Security","url":"https://www.academia.edu/Documents/in/Network_Security?f_ri=14118","nofollow":true},{"id":6078,"name":"Visual Analytics","url":"https://www.academia.edu/Documents/in/Visual_Analytics?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":32373,"name":"Programming Language Design","url":"https://www.academia.edu/Documents/in/Programming_Language_Design?f_ri=14118"},{"id":597570,"name":"Programming Model","url":"https://www.academia.edu/Documents/in/Programming_Model?f_ri=14118"},{"id":3085171,"name":"runtime system","url":"https://www.academia.edu/Documents/in/runtime_system?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_55345560" data-work_id="55345560" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/55345560/Page_based_Distributed_Shared_Memory_for_OSF_DCE">Page-based Distributed Shared Memory for OSF/DCE</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Distributed shared memory systems strive to overcome the architectural limitations of shared memory computers and to make easier developing parallel programs in distributed environment. As is known, however, in order to meet these goals... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_55345560" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Distributed shared memory systems strive to overcome the architectural limitations of shared memory computers and to make easier developing parallel programs in distributed environment. As is known, however, in order to meet these goals in practice many specific and difficult problems have to be solved. In this paper fundamentals of DSM systems' construction-including basic design, mechanisms, memory consistency models, and problems-are presented. Then, the general concept and hierarchical structure of page-based DSM system for UNIX and OSF/DCE platforms, have been proposed. Applications of the basic DCE components for improving security, modularity, scalability and portability of the proposed system in comparison with the existing ones, have been described.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/55345560" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="374d8f2291c63051cc2f6da104ef5d99" rel="nofollow" data-download="{"attachment_id":71258795,"asset_id":55345560,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/71258795/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="46496526" href="https://poznan.academia.edu/Micha%C5%82Szychowiak">Michał Szychowiak</a><script data-card-contents-for-user="46496526" type="text/json">{"id":46496526,"first_name":"Michał","last_name":"Szychowiak","domain_name":"poznan","page_name":"MichałSzychowiak","display_name":"Michał Szychowiak","profile_url":"https://poznan.academia.edu/Micha%C5%82Szychowiak?f_ri=14118","photo":"https://0.academia-photos.com/46496526/12297989/13692399/s65_micha_.szychowiak.jpg"}</script></span></span></li><li class="js-paper-rank-work_55345560 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="55345560"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 55345560, container: ".js-paper-rank-work_55345560", }); 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$(".js-view-count[data-work-id=55345560]").text(description); $(".js-view-count-work_55345560").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_55345560").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="55345560"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="97733" rel="nofollow" href="https://www.academia.edu/Documents/in/Shared_memory">Shared memory</a><script data-card-contents-for-ri="97733" type="text/json">{"id":97733,"name":"Shared memory","url":"https://www.academia.edu/Documents/in/Shared_memory?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=55345560]'), work: {"id":55345560,"title":"Page-based Distributed Shared Memory for OSF/DCE","created_at":"2021-10-04T00:35:36.254-07:00","url":"https://www.academia.edu/55345560/Page_based_Distributed_Shared_Memory_for_OSF_DCE?f_ri=14118","dom_id":"work_55345560","summary":"Distributed shared memory systems strive to overcome the architectural limitations of shared memory computers and to make easier developing parallel programs in distributed environment. As is known, however, in order to meet these goals in practice many specific and difficult problems have to be solved. In this paper fundamentals of DSM systems' construction-including basic design, mechanisms, memory consistency models, and problems-are presented. Then, the general concept and hierarchical structure of page-based DSM system for UNIX and OSF/DCE platforms, have been proposed. Applications of the basic DCE components for improving security, modularity, scalability and portability of the proposed system in comparison with the existing ones, have been described.","downloadable_attachments":[{"id":71258795,"asset_id":55345560,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":46496526,"first_name":"Michał","last_name":"Szychowiak","domain_name":"poznan","page_name":"MichałSzychowiak","display_name":"Michał Szychowiak","profile_url":"https://poznan.academia.edu/Micha%C5%82Szychowiak?f_ri=14118","photo":"https://0.academia-photos.com/46496526/12297989/13692399/s65_micha_.szychowiak.jpg"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":97733,"name":"Shared memory","url":"https://www.academia.edu/Documents/in/Shared_memory?f_ri=14118","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_8882501" data-work_id="8882501" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/8882501/A_Simple_DSM_System_Design_and_Implementation">A Simple DSM System Design and Implementation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper, a Simple Distributed Shared Memory (SDSM) system, a hybrid version of shared memory and message passing version is proposed. This version effectively uses the benefits of shared memory in terms of easiness of programming... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_8882501" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, a Simple Distributed Shared Memory (SDSM) system, a hybrid version of shared memory and message passing version is proposed. This version effectively uses the benefits of shared memory in terms of easiness of programming and message passing in terms of efficiency. Further it is designed to effectively utilize the state-of-art multicore based network of workstations and supports standard pthread interface and OpenMP model for writing shared memory programs as well as MPI interface for writing message passing programs. This system has been studied using standard SPLASH-2 (Stanford ParalleL Applications for SHared memory -2), NPB(NAS Parallel Benchmarks), IMB (Intel MPI Benchmarks) benchmarks and some well known parallel algorithms. Its performance has been compared with JIAJIA DSM system that uses efficient scope consistency model for shared memory programs and with MPI library on network of Linux systems for MPI programs. In many cases it was found to perform much better than those systems.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/8882501" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9bb2d019f6c93d138f7649016f708817" rel="nofollow" data-download="{"attachment_id":35214985,"asset_id":8882501,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/35214985/download_file?st=MTczOTcxNDI1OSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="19485314" href="https://nitw.academia.edu/ChapramSudhakar">Chapram Sudhakar</a><script data-card-contents-for-user="19485314" type="text/json">{"id":19485314,"first_name":"Chapram","last_name":"Sudhakar","domain_name":"nitw","page_name":"ChapramSudhakar","display_name":"Chapram Sudhakar","profile_url":"https://nitw.academia.edu/ChapramSudhakar?f_ri=14118","photo":"https://0.academia-photos.com/19485314/5430280/6193632/s65_chapram.sudhakar.jpg_oh_1d350bf4a0a5e64b079b157caa34eed5_oe_54e7e0d8___gda___1421671486_78a0370d292d50c9612e848030a85abc"}</script></span></span></li><li class="js-paper-rank-work_8882501 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="8882501"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 8882501, container: ".js-paper-rank-work_8882501", }); 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$(".js-view-count[data-work-id=8882501]").text(description); $(".js-view-count-work_8882501").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_8882501").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="8882501"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="75769" rel="nofollow" href="https://www.academia.edu/Documents/in/OpenMP">OpenMP</a><script data-card-contents-for-ri="75769" type="text/json">{"id":75769,"name":"OpenMP","url":"https://www.academia.edu/Documents/in/OpenMP?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=8882501]'), work: {"id":8882501,"title":"A Simple DSM System Design and Implementation","created_at":"2014-10-20T21:54:23.970-07:00","url":"https://www.academia.edu/8882501/A_Simple_DSM_System_Design_and_Implementation?f_ri=14118","dom_id":"work_8882501","summary":"In this paper, a Simple Distributed Shared Memory (SDSM) system, a hybrid version of shared memory and message passing version is proposed. This version effectively uses the benefits of shared memory in terms of easiness of programming and message passing in terms of efficiency. Further it is designed to effectively utilize the state-of-art multicore based network of workstations and supports standard pthread interface and OpenMP model for writing shared memory programs as well as MPI interface for writing message passing programs. This system has been studied using standard SPLASH-2 (Stanford ParalleL Applications for SHared memory -2), NPB(NAS Parallel Benchmarks), IMB (Intel MPI Benchmarks) benchmarks and some well known parallel algorithms. Its performance has been compared with JIAJIA DSM system that uses efficient scope consistency model for shared memory programs and with MPI library on network of Linux systems for MPI programs. In many cases it was found to perform much better than those systems.","downloadable_attachments":[{"id":35214985,"asset_id":8882501,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":19485314,"first_name":"Chapram","last_name":"Sudhakar","domain_name":"nitw","page_name":"ChapramSudhakar","display_name":"Chapram Sudhakar","profile_url":"https://nitw.academia.edu/ChapramSudhakar?f_ri=14118","photo":"https://0.academia-photos.com/19485314/5430280/6193632/s65_chapram.sudhakar.jpg_oh_1d350bf4a0a5e64b079b157caa34eed5_oe_54e7e0d8___gda___1421671486_78a0370d292d50c9612e848030a85abc"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":75769,"name":"OpenMP","url":"https://www.academia.edu/Documents/in/OpenMP?f_ri=14118","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_890831" data-work_id="890831" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/890831/Integrating_coordinated_checkpointing_and_recovery_mechanisms_into_DSM_synchronization_barriers">Integrating coordinated checkpointing and recovery mechanisms into DSM synchronization barriers</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">CITATIONS 0 READS 17 2 authors, including: Some of the authors of this publication are also working on these related projects: Distributed systems View project</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm 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data-card-contents-for-ri="305" type="text/json">{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="19997" rel="nofollow" href="https://www.academia.edu/Documents/in/Pure_Mathematics">Pure Mathematics</a>, <script data-card-contents-for-ri="19997" type="text/json">{"id":19997,"name":"Pure Mathematics","url":"https://www.academia.edu/Documents/in/Pure_Mathematics?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" 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project","downloadable_attachments":[{"id":51179049,"asset_id":890831,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":712013,"first_name":"Alba Cristina","last_name":"Magalhaes Alves de Melo","domain_name":"unb","page_name":"AlbaCristinaMagalhaesAlvesdeMelo","display_name":"Alba Cristina Magalhaes Alves de Melo","profile_url":"https://unb.academia.edu/AlbaCristinaMagalhaesAlvesdeMelo?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":19997,"name":"Pure Mathematics","url":"https://www.academia.edu/Documents/in/Pure_Mathematics?f_ri=14118","nofollow":true},{"id":97733,"name":"Shared memory","url":"https://www.academia.edu/Documents/in/Shared_memory?f_ri=14118","nofollow":true},{"id":921109,"name":"Wea","url":"https://www.academia.edu/Documents/in/Wea?f_ri=14118"},{"id":1939392,"name":"Experimental Algorithmics","url":"https://www.academia.edu/Documents/in/Experimental_Algorithmics?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_2940080" data-work_id="2940080" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/2940080/Parallel_smith_waterman_algorithm_for_local_dna_comparison_in_a_cluster_of_workstations">Parallel smith-waterman algorithm for local dna comparison in a cluster of workstations</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">The title phrase, a Greek version of "games children play", is a common classroom example of a syntactic peculiarity (singular verb form with neutral plural subject) in the Attic dialect of ancient Greek.</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/2940080" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="476c427949f32d58dcc2cf9c078d7f5f" rel="nofollow" data-download="{"attachment_id":30898672,"asset_id":2940080,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm 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class="u-positionAbsolute" data-has-card-for-ri-list="2940080"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">10</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="146" rel="nofollow" href="https://www.academia.edu/Documents/in/Bioinformatics">Bioinformatics</a>, <script data-card-contents-for-ri="146" type="text/json">{"id":146,"name":"Bioinformatics","url":"https://www.academia.edu/Documents/in/Bioinformatics?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4233" rel="nofollow" href="https://www.academia.edu/Documents/in/Computational_Biology">Computational Biology</a>, <script data-card-contents-for-ri="4233" type="text/json">{"id":4233,"name":"Computational Biology","url":"https://www.academia.edu/Documents/in/Computational_Biology?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="22686" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_System">Distributed System</a><script data-card-contents-for-ri="22686" type="text/json">{"id":22686,"name":"Distributed System","url":"https://www.academia.edu/Documents/in/Distributed_System?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=2940080]'), work: {"id":2940080,"title":"Parallel smith-waterman algorithm for local dna comparison in a cluster of 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(2004) A range of memory possibilities: The challenge of the false memory debate for clinicians and researchers.</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The aim of this article is to present a succinct review and evaluation of the main areas of contention in the false memory debate and, from this basis, to suggest ways in which the best from both sides can be utilized. We examine the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_2326347" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The aim of this article is to present a succinct review and evaluation of the main areas of contention in the false memory debate and, from this basis, to suggest ways in which the best from both sides can be utilized. We examine the potential pitfalls of therapy in terms of the fallibility and suggestibility of autobiographical memory and therapists and therapeutic techniques as the architects of false memories. We then evaluate the case for false memory formation examining if some researchers hold misconceived views of psychotherapy, if experimental studies lack ecological validity, and the effect of trauma on memory. Finally, we explore how the potential pitfalls of therapy can be avoided in practice, reflecting on the usefulness of British Psychological Society guidelines, how clinicians can implement research findings, and how research on the false memory debate can be improved. We conclude that the way forward is researcher–clinician collaboration in the development of ecologically valid research paradigms.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/2326347" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9f7e386ea7faf632d9a343d53598eb6c" rel="nofollow" data-download="{"attachment_id":31245614,"asset_id":2326347,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/31245614/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="231432" href="https://leeds.academia.edu/AnnaMadill">Anna Madill</a><script data-card-contents-for-user="231432" type="text/json">{"id":231432,"first_name":"Anna","last_name":"Madill","domain_name":"leeds","page_name":"AnnaMadill","display_name":"Anna Madill","profile_url":"https://leeds.academia.edu/AnnaMadill?f_ri=14118","photo":"https://0.academia-photos.com/231432/153122/178163/s65_anna.madill.jpg"}</script></span></span></li><li class="js-paper-rank-work_2326347 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="2326347"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 2326347, container: ".js-paper-rank-work_2326347", }); 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They describe how a single... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_21261246" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The authors goal is to be able to predict the performance of a parallel program early in the program development process; to that end they require prediction methods that can be based on incomplete programs. They describe how a single method based on communication-to-computation (C/C) ratio can be used to predict performance accurately and yet fairly simply in some commonly encountered cases. 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href="https://www.academia.edu/521170/Uniformization_and_hypergraph_partitioning_for_the_distributed_computation_of_response_time_densities_in_very_large_Markov_models">Uniformization and hypergraph partitioning for the distributed computation of response time densities in very large Markov models</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/521170" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="93144390b56ed61b4aee3306ce72344d" rel="nofollow" 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});</script></span><script>$(function() { $(".js-view-count-work_521170").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="521170"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="440" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="39698" rel="nofollow" href="https://www.academia.edu/Documents/in/Markov_Models">Markov Models</a>, <script data-card-contents-for-ri="39698" type="text/json">{"id":39698,"name":"Markov Models","url":"https://www.academia.edu/Documents/in/Markov_Models?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="42555" rel="nofollow" href="https://www.academia.edu/Documents/in/Communication_System">Communication System</a><script data-card-contents-for-ri="42555" type="text/json">{"id":42555,"name":"Communication System","url":"https://www.academia.edu/Documents/in/Communication_System?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=521170]'), work: {"id":521170,"title":"Uniformization and hypergraph partitioning for the distributed computation of response time densities in very large Markov models","created_at":"2011-04-10T05:51:23.282-07:00","url":"https://www.academia.edu/521170/Uniformization_and_hypergraph_partitioning_for_the_distributed_computation_of_response_time_densities_in_very_large_Markov_models?f_ri=14118","dom_id":"work_521170","summary":null,"downloadable_attachments":[{"id":2520684,"asset_id":521170,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3839,"first_name":"William","last_name":"Knottenbelt","domain_name":"imperial","page_name":"WilliamKnottenbelt","display_name":"William Knottenbelt","profile_url":"https://imperial.academia.edu/WilliamKnottenbelt?f_ri=14118","photo":"https://0.academia-photos.com/3839/1540/1619/s65_william.knottenbelt.jpg"}],"research_interests":[{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":39698,"name":"Markov Models","url":"https://www.academia.edu/Documents/in/Markov_Models?f_ri=14118","nofollow":true},{"id":42555,"name":"Communication System","url":"https://www.academia.edu/Documents/in/Communication_System?f_ri=14118","nofollow":true},{"id":45184,"name":"Homogenization","url":"https://www.academia.edu/Documents/in/Homogenization?f_ri=14118"},{"id":80870,"name":"Parallel \u0026 Distributed Computing","url":"https://www.academia.edu/Documents/in/Parallel_and_Distributed_Computing?f_ri=14118"},{"id":178055,"name":"Laplace Transform","url":"https://www.academia.edu/Documents/in/Laplace_Transform?f_ri=14118"},{"id":230429,"name":"Parallel","url":"https://www.academia.edu/Documents/in/Parallel?f_ri=14118"},{"id":348986,"name":"Parallel Algorithm","url":"https://www.academia.edu/Documents/in/Parallel_Algorithm?f_ri=14118"},{"id":372874,"name":"Transaction Processing","url":"https://www.academia.edu/Documents/in/Transaction_Processing?f_ri=14118"},{"id":500196,"name":"Hypergraph Partitioning","url":"https://www.academia.edu/Documents/in/Hypergraph_Partitioning?f_ri=14118"},{"id":545424,"name":"Load Balance","url":"https://www.academia.edu/Documents/in/Load_Balance?f_ri=14118"},{"id":595993,"name":"Markov chain","url":"https://www.academia.edu/Documents/in/Markov_chain?f_ri=14118"},{"id":702169,"name":"Parallel Computer","url":"https://www.academia.edu/Documents/in/Parallel_Computer?f_ri=14118"},{"id":1851903,"name":"Response Time","url":"https://www.academia.edu/Documents/in/Response_Time?f_ri=14118"},{"id":2050770,"name":"Markov model","url":"https://www.academia.edu/Documents/in/Markov_model?f_ri=14118"},{"id":2481338,"name":"distribution uniformity","url":"https://www.academia.edu/Documents/in/distribution_uniformity?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15045294 coauthored" data-work_id="15045294" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15045294/JaMP_an_implementation_of_OpenMP_for_a_Java_DSM">JaMP: an implementation of OpenMP for a Java DSM</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">In this paper we present JaMP, an adaptation of the OpenMP standard. JaMP is fitted to Jackal, a software-based DSM implementation for Java.</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15045294" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="bd2da9c9ad9b97ed1fbd4ace336abba8" rel="nofollow" data-download="{"attachment_id":43632802,"asset_id":15045294,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43632802/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34065702" href="https://independent.academia.edu/RonaldVeldema">Ronald Veldema</a><script data-card-contents-for-user="34065702" type="text/json">{"id":34065702,"first_name":"Ronald","last_name":"Veldema","domain_name":"independent","page_name":"RonaldVeldema","display_name":"Ronald Veldema","profile_url":"https://independent.academia.edu/RonaldVeldema?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-15045294">+1</span><div class="hidden js-additional-users-15045294"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/MatthiasBezold">Matthias Bezold</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-15045294'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-15045294').html(); 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JaMP is fitted to Jackal, a software-based DSM implementation for Java.","downloadable_attachments":[{"id":43632802,"asset_id":15045294,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34065702,"first_name":"Ronald","last_name":"Veldema","domain_name":"independent","page_name":"RonaldVeldema","display_name":"Ronald Veldema","profile_url":"https://independent.academia.edu/RonaldVeldema?f_ri=14118","photo":"/images/s65_no_pic.png"},{"id":34278040,"first_name":"Matthias","last_name":"Bezold","domain_name":"independent","page_name":"MatthiasBezold","display_name":"Matthias Bezold","profile_url":"https://independent.academia.edu/MatthiasBezold?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":64561,"name":"Computer Software","url":"https://www.academia.edu/Documents/in/Computer_Software?f_ri=14118","nofollow":true},{"id":70448,"name":"Fortran","url":"https://www.academia.edu/Documents/in/Fortran?f_ri=14118","nofollow":true},{"id":70648,"name":"Concurrency","url":"https://www.academia.edu/Documents/in/Concurrency?f_ri=14118"},{"id":97733,"name":"Shared memory","url":"https://www.academia.edu/Documents/in/Shared_memory?f_ri=14118"},{"id":116313,"name":"Automatic Parallelization","url":"https://www.academia.edu/Documents/in/Automatic_Parallelization?f_ri=14118"},{"id":171099,"name":"Java Language","url":"https://www.academia.edu/Documents/in/Java_Language?f_ri=14118"},{"id":254626,"name":"Cluster","url":"https://www.academia.edu/Documents/in/Cluster?f_ri=14118"},{"id":1489478,"name":"Programming language","url":"https://www.academia.edu/Documents/in/Programming_language?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_19097265" data-work_id="19097265" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/19097265/Hybrid_MPI_OpenMP_parallel_programming_on_clusters_of_multi_core_SMP_nodes">Hybrid MPI/OpenMP parallel programming on clusters of multi-core SMP nodes</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Today most systems in high-performance computing (HPC) feature a hierarchical hardware design: Shared memory nodes with several multi-core CPUs are connected via a network infrastructure. Parallel programming must combine distributed... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_19097265" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Today most systems in high-performance computing (HPC) feature a hierarchical hardware design: Shared memory nodes with several multi-core CPUs are connected via a network infrastructure. Parallel programming must combine distributed memory parallelization on the node interconnect with shared memory parallelization inside each node. We describe potentials and challenges of the dominant programming models on hierarchically structured hardware: Pure MPI (Message Passing Interface), pure OpenMP (with distributed shared memory extensions) and hybrid MPI+OpenMP in several flavors. We pinpoint cases where a hybrid programming model can indeed be the superior solution because of reduced communication needs and memory consumption, or improved load balance. Furthermore we show that machine topology has a significant impact on performance for all parallelization strategies and that topology awareness should be built into all applications in the future. Finally we give an outlook on possible standardization goals and extensions that could make hybrid programming easier to do with performance in mind.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/19097265" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="306f60d3756d89a689db9d5fef17f290" rel="nofollow" data-download="{"attachment_id":40429551,"asset_id":19097265,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40429551/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="39279325" href="https://independent.academia.edu/RolfRabenseifner">Rolf Rabenseifner</a><script data-card-contents-for-user="39279325" type="text/json">{"id":39279325,"first_name":"Rolf","last_name":"Rabenseifner","domain_name":"independent","page_name":"RolfRabenseifner","display_name":"Rolf Rabenseifner","profile_url":"https://independent.academia.edu/RolfRabenseifner?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_19097265 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="19097265"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 19097265, container: ".js-paper-rank-work_19097265", }); });</script></li><li class="js-percentile-work_19097265 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19097265; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_19097265"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_19097265 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="19097265"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19097265; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19097265]").text(description); $(".js-view-count-work_19097265").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_19097265").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="19097265"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="77597" rel="nofollow" href="https://www.academia.edu/Documents/in/Hardware_Design">Hardware Design</a>, <script data-card-contents-for-ri="77597" type="text/json">{"id":77597,"name":"Hardware Design","url":"https://www.academia.edu/Documents/in/Hardware_Design?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="80870" rel="nofollow" href="https://www.academia.edu/Documents/in/Parallel_and_Distributed_Computing">Parallel & Distributed Computing</a>, <script data-card-contents-for-ri="80870" type="text/json">{"id":80870,"name":"Parallel \u0026 Distributed Computing","url":"https://www.academia.edu/Documents/in/Parallel_and_Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="97733" rel="nofollow" href="https://www.academia.edu/Documents/in/Shared_memory">Shared memory</a><script data-card-contents-for-ri="97733" type="text/json">{"id":97733,"name":"Shared memory","url":"https://www.academia.edu/Documents/in/Shared_memory?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=19097265]'), work: {"id":19097265,"title":"Hybrid MPI/OpenMP parallel programming on clusters of multi-core SMP nodes","created_at":"2015-11-27T06:50:46.654-08:00","url":"https://www.academia.edu/19097265/Hybrid_MPI_OpenMP_parallel_programming_on_clusters_of_multi_core_SMP_nodes?f_ri=14118","dom_id":"work_19097265","summary":"Today most systems in high-performance computing (HPC) feature a hierarchical hardware design: Shared memory nodes with several multi-core CPUs are connected via a network infrastructure. Parallel programming must combine distributed memory parallelization on the node interconnect with shared memory parallelization inside each node. We describe potentials and challenges of the dominant programming models on hierarchically structured hardware: Pure MPI (Message Passing Interface), pure OpenMP (with distributed shared memory extensions) and hybrid MPI+OpenMP in several flavors. We pinpoint cases where a hybrid programming model can indeed be the superior solution because of reduced communication needs and memory consumption, or improved load balance. Furthermore we show that machine topology has a significant impact on performance for all parallelization strategies and that topology awareness should be built into all applications in the future. Finally we give an outlook on possible standardization goals and extensions that could make hybrid programming easier to do with performance in mind.","downloadable_attachments":[{"id":40429551,"asset_id":19097265,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":39279325,"first_name":"Rolf","last_name":"Rabenseifner","domain_name":"independent","page_name":"RolfRabenseifner","display_name":"Rolf Rabenseifner","profile_url":"https://independent.academia.edu/RolfRabenseifner?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":77597,"name":"Hardware Design","url":"https://www.academia.edu/Documents/in/Hardware_Design?f_ri=14118","nofollow":true},{"id":80870,"name":"Parallel \u0026 Distributed Computing","url":"https://www.academia.edu/Documents/in/Parallel_and_Distributed_Computing?f_ri=14118","nofollow":true},{"id":97733,"name":"Shared memory","url":"https://www.academia.edu/Documents/in/Shared_memory?f_ri=14118","nofollow":true},{"id":545424,"name":"Load Balance","url":"https://www.academia.edu/Documents/in/Load_Balance?f_ri=14118"},{"id":597570,"name":"Programming Model","url":"https://www.academia.edu/Documents/in/Programming_Model?f_ri=14118"},{"id":733999,"name":"Message Passing Interface","url":"https://www.academia.edu/Documents/in/Message_Passing_Interface?f_ri=14118"},{"id":850706,"name":"High performance computer","url":"https://www.academia.edu/Documents/in/High_performance_computer?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_6498732" data-work_id="6498732" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/6498732/JETri_ANALISIS_UNJUK_KERJA_KOMPUTASI_DISTRIBUTED_SHARED_MEMORY_PADA_SISTEM_CLUSTER_KOMPUTER_PERSONAL">JETri, ANALISIS UNJUK KERJA KOMPUTASI DISTRIBUTED SHARED MEMORY PADA SISTEM CLUSTER KOMPUTER PERSONAL</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Parallel processing systems that support the shared memory abstraction are becoming widely accepted in many areas of computing. The shared memory or single system image of address space abstraction provides several advantages over the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_6498732" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Parallel processing systems that support the shared memory abstraction are becoming widely accepted in many areas of computing. The shared memory or single system image of address space abstraction provides several advantages over the message passing abstraction. Distributed shared memory or DSM is a memory configuration system that can be used to provide a coherent shared address space for a workstation clusters or loosely coupled systems which have no support for shared memory in hardware.The purpose of this paper is to describe issues related to the implementation of DSM in Personal Computer Cluster system. The system is developed under Linux operating system and cluster middleware Parallel Virtual Machine [PVM]. In this research, an object-based Distributed Shared Memory (DSM) system called Adsmith is used on the top of PVM. An application program for A Traveling Salesman Problem (TSP) is tested using different scenarios to get average execution time and speedup of the computation. Each scenario represents a different number of node cluster involved for computation. The results show that programs developed with Adsmith can achieve better performance comparable to that developed with PVM in high computation environment.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/6498732" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="799c21b1cb2af831ecc1fa890c6071e6" rel="nofollow" data-download="{"attachment_id":33278468,"asset_id":6498732,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/33278468/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="10351707" href="https://uinsuska.academia.edu/ArifNorhidayat">Arif Norhidayat</a><script data-card-contents-for-user="10351707" type="text/json">{"id":10351707,"first_name":"Arif","last_name":"Norhidayat","domain_name":"uinsuska","page_name":"ArifNorhidayat","display_name":"Arif Norhidayat","profile_url":"https://uinsuska.academia.edu/ArifNorhidayat?f_ri=14118","photo":"https://0.academia-photos.com/10351707/11411640/12728742/s65_arif.norhidayat.jpg"}</script></span></span></li><li class="js-paper-rank-work_6498732 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="6498732"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 6498732, container: ".js-paper-rank-work_6498732", }); });</script></li><li class="js-percentile-work_6498732 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 6498732; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_6498732"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_6498732 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="6498732"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 6498732; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=6498732]").text(description); $(".js-view-count-work_6498732").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_6498732").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="6498732"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="329" rel="nofollow" href="https://www.academia.edu/Documents/in/Algebra">Algebra</a>, <script data-card-contents-for-ri="329" type="text/json">{"id":329,"name":"Algebra","url":"https://www.academia.edu/Documents/in/Algebra?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="451" rel="nofollow" href="https://www.academia.edu/Documents/in/Programming_Languages">Programming Languages</a>, <script data-card-contents-for-ri="451" type="text/json">{"id":451,"name":"Programming Languages","url":"https://www.academia.edu/Documents/in/Programming_Languages?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="523" rel="nofollow" href="https://www.academia.edu/Documents/in/Chemistry">Chemistry</a>, <script data-card-contents-for-ri="523" type="text/json">{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1000" rel="nofollow" href="https://www.academia.edu/Documents/in/Instructional_Design">Instructional Design</a><script data-card-contents-for-ri="1000" type="text/json">{"id":1000,"name":"Instructional Design","url":"https://www.academia.edu/Documents/in/Instructional_Design?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=6498732]'), work: {"id":6498732,"title":"JETri, ANALISIS UNJUK KERJA KOMPUTASI DISTRIBUTED SHARED MEMORY PADA SISTEM CLUSTER KOMPUTER PERSONAL","created_at":"2014-03-21T13:10:41.131-07:00","url":"https://www.academia.edu/6498732/JETri_ANALISIS_UNJUK_KERJA_KOMPUTASI_DISTRIBUTED_SHARED_MEMORY_PADA_SISTEM_CLUSTER_KOMPUTER_PERSONAL?f_ri=14118","dom_id":"work_6498732","summary":"Parallel processing systems that support the shared memory abstraction are becoming widely accepted in many areas of computing. The shared memory or single system image of address space abstraction provides several advantages over the message passing abstraction. Distributed shared memory or DSM is a memory configuration system that can be used to provide a coherent shared address space for a workstation clusters or loosely coupled systems which have no support for shared memory in hardware.The purpose of this paper is to describe issues related to the implementation of DSM in Personal Computer Cluster system. The system is developed under Linux operating system and cluster middleware Parallel Virtual Machine [PVM]. In this research, an object-based Distributed Shared Memory (DSM) system called Adsmith is used on the top of PVM. An application program for A Traveling Salesman Problem (TSP) is tested using different scenarios to get average execution time and speedup of the computation. Each scenario represents a different number of node cluster involved for computation. The results show that programs developed with Adsmith can achieve better performance comparable to that developed with PVM in high computation environment.","downloadable_attachments":[{"id":33278468,"asset_id":6498732,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":10351707,"first_name":"Arif","last_name":"Norhidayat","domain_name":"uinsuska","page_name":"ArifNorhidayat","display_name":"Arif Norhidayat","profile_url":"https://uinsuska.academia.edu/ArifNorhidayat?f_ri=14118","photo":"https://0.academia-photos.com/10351707/11411640/12728742/s65_arif.norhidayat.jpg"}],"research_interests":[{"id":329,"name":"Algebra","url":"https://www.academia.edu/Documents/in/Algebra?f_ri=14118","nofollow":true},{"id":451,"name":"Programming Languages","url":"https://www.academia.edu/Documents/in/Programming_Languages?f_ri=14118","nofollow":true},{"id":523,"name":"Chemistry","url":"https://www.academia.edu/Documents/in/Chemistry?f_ri=14118","nofollow":true},{"id":1000,"name":"Instructional Design","url":"https://www.academia.edu/Documents/in/Instructional_Design?f_ri=14118","nofollow":true},{"id":1372,"name":"Architecture","url":"https://www.academia.edu/Documents/in/Architecture?f_ri=14118"},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_36912962" data-work_id="36912962" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/36912962/Sparse_Block_and_Cyclic_Data_Distributions_for_Matrix_Computations">Sparse Block and Cyclic Data Distributions for Matrix Computations</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A significant part of scientific codes consist of sparse matrix computations. In this work we propose two new pseudoregular data distributions for sparse matrices. The Multiple Recursive Decomposition (MRD) partitions the data using the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_36912962" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A significant part of scientific codes consist of sparse matrix computations. In this work we propose two new pseudoregular data distributions for sparse matrices. The Multiple Recursive Decomposition (MRD) partitions the data using the prime factors of the dimensions of a multiprocessor network with mesh topology. Furthermore, we introduce a new storage scheme, storage-by-row-of-blocks, that significantly increases the efficiency of the Scatter distribution. We will name Block Row Scatter (BRS) distribution this new variant. The MRD and BRS methods achieve results that improve those obtained by other analyzed methods, being their implementation easier. In fact, the data distributions resulting from the MRD and BRS methods are a generalization of the Block and Cyclic distributions used in dense matrices.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/36912962" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2a8c18729f88557746d23687bdab6b60" rel="nofollow" data-download="{"attachment_id":56863767,"asset_id":36912962,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56863767/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="83462083" href="https://independent.academia.edu/LuisRomero404">Luis Romero</a><script data-card-contents-for-user="83462083" type="text/json">{"id":83462083,"first_name":"Luis","last_name":"Romero","domain_name":"independent","page_name":"LuisRomero404","display_name":"Luis Romero","profile_url":"https://independent.academia.edu/LuisRomero404?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_36912962 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="36912962"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 36912962, container: ".js-paper-rank-work_36912962", }); });</script></li><li class="js-percentile-work_36912962 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 36912962; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_36912962"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_36912962 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="36912962"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 36912962; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=36912962]").text(description); $(".js-view-count-work_36912962").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_36912962").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="36912962"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="442" rel="nofollow" href="https://www.academia.edu/Documents/in/Parallel_Computing">Parallel Computing</a>, <script data-card-contents-for-ri="442" type="text/json">{"id":442,"name":"Parallel Computing","url":"https://www.academia.edu/Documents/in/Parallel_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="394067" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Distribution">Data Distribution</a>, <script data-card-contents-for-ri="394067" type="text/json">{"id":394067,"name":"Data Distribution","url":"https://www.academia.edu/Documents/in/Data_Distribution?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2150506" rel="nofollow" href="https://www.academia.edu/Documents/in/Processing_Element">Processing Element</a><script data-card-contents-for-ri="2150506" type="text/json">{"id":2150506,"name":"Processing Element","url":"https://www.academia.edu/Documents/in/Processing_Element?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=36912962]'), work: {"id":36912962,"title":"Sparse Block and Cyclic Data Distributions for Matrix Computations","created_at":"2018-06-25T04:50:58.721-07:00","url":"https://www.academia.edu/36912962/Sparse_Block_and_Cyclic_Data_Distributions_for_Matrix_Computations?f_ri=14118","dom_id":"work_36912962","summary":"A significant part of scientific codes consist of sparse matrix computations. In this work we propose two new pseudoregular data distributions for sparse matrices. The Multiple Recursive Decomposition (MRD) partitions the data using the prime factors of the dimensions of a multiprocessor network with mesh topology. Furthermore, we introduce a new storage scheme, storage-by-row-of-blocks, that significantly increases the efficiency of the Scatter distribution. We will name Block Row Scatter (BRS) distribution this new variant. The MRD and BRS methods achieve results that improve those obtained by other analyzed methods, being their implementation easier. In fact, the data distributions resulting from the MRD and BRS methods are a generalization of the Block and Cyclic distributions used in dense matrices.","downloadable_attachments":[{"id":56863767,"asset_id":36912962,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":83462083,"first_name":"Luis","last_name":"Romero","domain_name":"independent","page_name":"LuisRomero404","display_name":"Luis Romero","profile_url":"https://independent.academia.edu/LuisRomero404?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":442,"name":"Parallel Computing","url":"https://www.academia.edu/Documents/in/Parallel_Computing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":394067,"name":"Data Distribution","url":"https://www.academia.edu/Documents/in/Data_Distribution?f_ri=14118","nofollow":true},{"id":2150506,"name":"Processing Element","url":"https://www.academia.edu/Documents/in/Processing_Element?f_ri=14118","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_5490023" data-work_id="5490023" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/5490023/Strategies_for_Parallelizing_KMeans_Data_Clustering_Algorithm">Strategies for Parallelizing KMeans Data Clustering Algorithm</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Data Clustering is a descriptive data mining task of finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups [5]. The... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_5490023" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Data Clustering is a descriptive data mining task of finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups [5]. The motivation behind this research paper is to explore KMeans partitioning algorithm in the currently available parallel architecture using parallel programming models. Parallel KMeans algorithms have been implemented for a shared memory model using OpenMP programming and distributed memory model using MPI programming. A hybrid version of OpenMP in MPI programming also has been experimented. The performance of the parallel algorithms were analysed to compare the speedup obtained and to study the Amdhals effect. The computational time of hybrid method was reduced by 50% compared to MPI and was also more efficient with balanced load.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/5490023" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0b9f408f388d06ddc1455f2a24fa4e0d" rel="nofollow" data-download="{"attachment_id":36763028,"asset_id":5490023,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/36763028/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7726828" href="https://ssn-in.academia.edu/MohanavalliS">Mohanavalli S.</a><script data-card-contents-for-user="7726828" type="text/json">{"id":7726828,"first_name":"Mohanavalli","last_name":"S.","domain_name":"ssn-in","page_name":"MohanavalliS","display_name":"Mohanavalli S.","profile_url":"https://ssn-in.academia.edu/MohanavalliS?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_5490023 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="5490023"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 5490023, container: ".js-paper-rank-work_5490023", }); });</script></li><li class="js-percentile-work_5490023 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 5490023; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_5490023"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_5490023 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="5490023"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 5490023; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=5490023]").text(description); $(".js-view-count-work_5490023").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_5490023").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="5490023"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="97733" rel="nofollow" href="https://www.academia.edu/Documents/in/Shared_memory">Shared memory</a>, <script data-card-contents-for-ri="97733" type="text/json">{"id":97733,"name":"Shared memory","url":"https://www.academia.edu/Documents/in/Shared_memory?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="142311" rel="nofollow" href="https://www.academia.edu/Documents/in/Research_Paper">Research Paper</a><script data-card-contents-for-ri="142311" type="text/json">{"id":142311,"name":"Research Paper","url":"https://www.academia.edu/Documents/in/Research_Paper?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=5490023]'), work: {"id":5490023,"title":"Strategies for Parallelizing KMeans Data Clustering Algorithm","created_at":"2013-12-20T07:40:18.516-08:00","url":"https://www.academia.edu/5490023/Strategies_for_Parallelizing_KMeans_Data_Clustering_Algorithm?f_ri=14118","dom_id":"work_5490023","summary":"Data Clustering is a descriptive data mining task of finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups [5]. The motivation behind this research paper is to explore KMeans partitioning algorithm in the currently available parallel architecture using parallel programming models. Parallel KMeans algorithms have been implemented for a shared memory model using OpenMP programming and distributed memory model using MPI programming. A hybrid version of OpenMP in MPI programming also has been experimented. The performance of the parallel algorithms were analysed to compare the speedup obtained and to study the Amdhals effect. The computational time of hybrid method was reduced by 50% compared to MPI and was also more efficient with balanced load.","downloadable_attachments":[{"id":36763028,"asset_id":5490023,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7726828,"first_name":"Mohanavalli","last_name":"S.","domain_name":"ssn-in","page_name":"MohanavalliS","display_name":"Mohanavalli S.","profile_url":"https://ssn-in.academia.edu/MohanavalliS?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":97733,"name":"Shared memory","url":"https://www.academia.edu/Documents/in/Shared_memory?f_ri=14118","nofollow":true},{"id":142311,"name":"Research Paper","url":"https://www.academia.edu/Documents/in/Research_Paper?f_ri=14118","nofollow":true},{"id":199424,"name":"Data Clustering","url":"https://www.academia.edu/Documents/in/Data_Clustering?f_ri=14118"},{"id":348986,"name":"Parallel Algorithm","url":"https://www.academia.edu/Documents/in/Parallel_Algorithm?f_ri=14118"},{"id":1745713,"name":"Parallel Architecture","url":"https://www.academia.edu/Documents/in/Parallel_Architecture?f_ri=14118"},{"id":1911488,"name":"Hybrid Method","url":"https://www.academia.edu/Documents/in/Hybrid_Method?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_9503532" data-work_id="9503532" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/9503532/A_survey_of_urban_vehicular_sensing_platforms">A survey of urban vehicular sensing platforms</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">a b s t r a c t Vehicular sensing where vehicles on the road continuously gather, process, and share location-relevant sensor data (e.g., road condition, traffic flow) is emerging as a new network paradigm for sensor information sharing... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_9503532" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">a b s t r a c t Vehicular sensing where vehicles on the road continuously gather, process, and share location-relevant sensor data (e.g., road condition, traffic flow) is emerging as a new network paradigm for sensor information sharing in urban environments. Recently, smartphones have also received a lot of attention for their potential as portable vehicular urban sensing platforms, as they are equipped with a variety of environment and motion sensors (e.g., audio/video, accelerometer, and GPS) and multiple wireless interfaces (e.g., WiFi, Bluetooth and 2/3G). The ability to take a smartphone on board a vehicle and to complement the sensors of the latter with advanced smartphone capabilities is of immense interest to the industry. In this paper we survey recent vehicular sensor network developments and identify new trends. In particular we review the way sensor information is collected, stored and harvested using inter-vehicular communications (e.g., mobility-assist mobility-assisted dissemination and geographic storage), as well using the infrastructure (e.g., centralized and distributed storage in the wired Internet). The comparative performance of the various sensing schemes is important to us. Thus, we review key results by carefully examining and explaining the evaluation methodology, in the process gaining insight into vehicular sensor network design. Our comparative study confirms that system performance is impacted by a variety of factors such as wireless access methods, mobility, user location, and popularity of the information.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/9503532" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="204804942ce3a4db83c13c737f319122" rel="nofollow" data-download="{"attachment_id":35732058,"asset_id":9503532,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/35732058/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="22281178" href="https://independent.academia.edu/EarthEntertainmentchannel">Earth Entertainment channel</a><script data-card-contents-for-user="22281178" type="text/json">{"id":22281178,"first_name":"Earth Entertainment","last_name":"channel","domain_name":"independent","page_name":"EarthEntertainmentchannel","display_name":"Earth Entertainment channel","profile_url":"https://independent.academia.edu/EarthEntertainmentchannel?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_9503532 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="9503532"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 9503532, container: ".js-paper-rank-work_9503532", }); 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$(".js-view-count[data-work-id=9503532]").text(description); $(".js-view-count-work_9503532").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_9503532").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="9503532"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">21</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="48" rel="nofollow" href="https://www.academia.edu/Documents/in/Engineering">Engineering</a>, <script data-card-contents-for-ri="48" type="text/json">{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="923" rel="nofollow" href="https://www.academia.edu/Documents/in/Technology">Technology</a>, <script data-card-contents-for-ri="923" type="text/json">{"id":923,"name":"Technology","url":"https://www.academia.edu/Documents/in/Technology?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4252" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Networks">Computer Networks</a>, <script data-card-contents-for-ri="4252" type="text/json">{"id":4252,"name":"Computer Networks","url":"https://www.academia.edu/Documents/in/Computer_Networks?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10194" rel="nofollow" href="https://www.academia.edu/Documents/in/Localization">Localization</a><script data-card-contents-for-ri="10194" type="text/json">{"id":10194,"name":"Localization","url":"https://www.academia.edu/Documents/in/Localization?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=9503532]'), work: {"id":9503532,"title":"A survey of urban vehicular sensing platforms","created_at":"2014-11-25T22:10:19.440-08:00","url":"https://www.academia.edu/9503532/A_survey_of_urban_vehicular_sensing_platforms?f_ri=14118","dom_id":"work_9503532","summary":"a b s t r a c t Vehicular sensing where vehicles on the road continuously gather, process, and share location-relevant sensor data (e.g., road condition, traffic flow) is emerging as a new network paradigm for sensor information sharing in urban environments. Recently, smartphones have also received a lot of attention for their potential as portable vehicular urban sensing platforms, as they are equipped with a variety of environment and motion sensors (e.g., audio/video, accelerometer, and GPS) and multiple wireless interfaces (e.g., WiFi, Bluetooth and 2/3G). The ability to take a smartphone on board a vehicle and to complement the sensors of the latter with advanced smartphone capabilities is of immense interest to the industry. In this paper we survey recent vehicular sensor network developments and identify new trends. In particular we review the way sensor information is collected, stored and harvested using inter-vehicular communications (e.g., mobility-assist mobility-assisted dissemination and geographic storage), as well using the infrastructure (e.g., centralized and distributed storage in the wired Internet). The comparative performance of the various sensing schemes is important to us. Thus, we review key results by carefully examining and explaining the evaluation methodology, in the process gaining insight into vehicular sensor network design. Our comparative study confirms that system performance is impacted by a variety of factors such as wireless access methods, mobility, user location, and popularity of the information.","downloadable_attachments":[{"id":35732058,"asset_id":9503532,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":22281178,"first_name":"Earth Entertainment","last_name":"channel","domain_name":"independent","page_name":"EarthEntertainmentchannel","display_name":"Earth Entertainment channel","profile_url":"https://independent.academia.edu/EarthEntertainmentchannel?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=14118","nofollow":true},{"id":923,"name":"Technology","url":"https://www.academia.edu/Documents/in/Technology?f_ri=14118","nofollow":true},{"id":4252,"name":"Computer Networks","url":"https://www.academia.edu/Documents/in/Computer_Networks?f_ri=14118","nofollow":true},{"id":10194,"name":"Localization","url":"https://www.academia.edu/Documents/in/Localization?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118"},{"id":23890,"name":"Comparative Study","url":"https://www.academia.edu/Documents/in/Comparative_Study?f_ri=14118"},{"id":85094,"name":"Distributed Storage","url":"https://www.academia.edu/Documents/in/Distributed_Storage?f_ri=14118"},{"id":88001,"name":"Information Sharing","url":"https://www.academia.edu/Documents/in/Information_Sharing?f_ri=14118"},{"id":98678,"name":"GPS TRACKING SYSTEM","url":"https://www.academia.edu/Documents/in/GPS_TRACKING_SYSTEM?f_ri=14118"},{"id":99052,"name":"Information Exchange","url":"https://www.academia.edu/Documents/in/Information_Exchange?f_ri=14118"},{"id":158569,"name":"Sensor Network","url":"https://www.academia.edu/Documents/in/Sensor_Network?f_ri=14118"},{"id":183166,"name":"Urban Environment","url":"https://www.academia.edu/Documents/in/Urban_Environment?f_ri=14118"},{"id":191765,"name":"Vehicular Communication","url":"https://www.academia.edu/Documents/in/Vehicular_Communication?f_ri=14118"},{"id":220371,"name":"Traffic Flow","url":"https://www.academia.edu/Documents/in/Traffic_Flow?f_ri=14118"},{"id":256233,"name":"Satellite Navigation","url":"https://www.academia.edu/Documents/in/Satellite_Navigation?f_ri=14118"},{"id":357198,"name":"Wireless access","url":"https://www.academia.edu/Documents/in/Wireless_access?f_ri=14118"},{"id":428860,"name":"Evaluation Methodology","url":"https://www.academia.edu/Documents/in/Evaluation_Methodology?f_ri=14118"},{"id":439526,"name":"Signal Detection","url":"https://www.academia.edu/Documents/in/Signal_Detection?f_ri=14118"},{"id":595034,"name":"Sensor Array","url":"https://www.academia.edu/Documents/in/Sensor_Array?f_ri=14118"},{"id":838973,"name":"System performance","url":"https://www.academia.edu/Documents/in/System_performance?f_ri=14118"},{"id":1191356,"name":"Internet","url":"https://www.academia.edu/Documents/in/Internet?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_16125379" data-work_id="16125379" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/16125379/Snow_A_Parallel_Computing_Framework_for_the_R_System">Snow: A Parallel Computing Framework for the R System</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper presents a simple parallel computing framework for the statistical programming language R. The system focuses on parallelization of familiar higher level mapping functions and emphasizes simplicity of use in order to encourage... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_16125379" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper presents a simple parallel computing framework for the statistical programming language R. The system focuses on parallelization of familiar higher level mapping functions and emphasizes simplicity of use in order to encourage adoption by a wide range of R users. The paper describes the design and implementation of the system, outlines examples of its use, and presents some possible directions for future developments.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/16125379" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4e84427445e194cd7cde56badb85b7ca" rel="nofollow" data-download="{"attachment_id":42740554,"asset_id":16125379,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42740554/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="35239606" href="https://washington.academia.edu/AnthonyRossini">Anthony Rossini</a><script data-card-contents-for-user="35239606" type="text/json">{"id":35239606,"first_name":"Anthony","last_name":"Rossini","domain_name":"washington","page_name":"AnthonyRossini","display_name":"Anthony Rossini","profile_url":"https://washington.academia.edu/AnthonyRossini?f_ri=14118","photo":"https://gravatar.com/avatar/ac2ed9b0b36372a53089e9c3ced2cc26?s=65"}</script></span></span></li><li class="js-paper-rank-work_16125379 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="16125379"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 16125379, container: ".js-paper-rank-work_16125379", }); 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$(".js-view-count[data-work-id=16125379]").text(description); $(".js-view-count-work_16125379").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_16125379").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="16125379"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="440" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3069" rel="nofollow" href="https://www.academia.edu/Documents/in/Parallel_Programming">Parallel Programming</a>, <script data-card-contents-for-ri="3069" type="text/json">{"id":3069,"name":"Parallel Programming","url":"https://www.academia.edu/Documents/in/Parallel_Programming?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="64561" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Software">Computer Software</a><script data-card-contents-for-ri="64561" type="text/json">{"id":64561,"name":"Computer Software","url":"https://www.academia.edu/Documents/in/Computer_Software?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=16125379]'), work: {"id":16125379,"title":"Snow: A Parallel Computing Framework for the R System","created_at":"2015-09-24T05:47:05.312-07:00","url":"https://www.academia.edu/16125379/Snow_A_Parallel_Computing_Framework_for_the_R_System?f_ri=14118","dom_id":"work_16125379","summary":"This paper presents a simple parallel computing framework for the statistical programming language R. The system focuses on parallelization of familiar higher level mapping functions and emphasizes simplicity of use in order to encourage adoption by a wide range of R users. The paper describes the design and implementation of the system, outlines examples of its use, and presents some possible directions for future developments.","downloadable_attachments":[{"id":42740554,"asset_id":16125379,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":35239606,"first_name":"Anthony","last_name":"Rossini","domain_name":"washington","page_name":"AnthonyRossini","display_name":"Anthony Rossini","profile_url":"https://washington.academia.edu/AnthonyRossini?f_ri=14118","photo":"https://gravatar.com/avatar/ac2ed9b0b36372a53089e9c3ced2cc26?s=65"}],"research_interests":[{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":3069,"name":"Parallel Programming","url":"https://www.academia.edu/Documents/in/Parallel_Programming?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":64561,"name":"Computer Software","url":"https://www.academia.edu/Documents/in/Computer_Software?f_ri=14118","nofollow":true},{"id":187402,"name":"Cross Validation","url":"https://www.academia.edu/Documents/in/Cross_Validation?f_ri=14118"},{"id":243826,"name":"Message Passing","url":"https://www.academia.edu/Documents/in/Message_Passing?f_ri=14118"},{"id":702169,"name":"Parallel Computer","url":"https://www.academia.edu/Documents/in/Parallel_Computer?f_ri=14118"},{"id":994520,"name":"Design and Implementation","url":"https://www.academia.edu/Documents/in/Design_and_Implementation?f_ri=14118"},{"id":1489478,"name":"Programming language","url":"https://www.academia.edu/Documents/in/Programming_language?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_7398589" data-work_id="7398589" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/7398589/A_parallel_Full_CI_algorithm">A parallel Full-CI algorithm</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A Full Configuration Interaction (Full-CI) algorithm is described. It is an integral-driven approach, with on-the-fly computation of the string-excitation lists that realize the application of the Hamiltonian to the Full-CI vector. The... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_7398589" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A Full Configuration Interaction (Full-CI) algorithm is described. It is an integral-driven approach, with on-the-fly computation of the string-excitation lists that realize the application of the Hamiltonian to the Full-CI vector. The algorithm has been implemented on vector and parallel architectures, both of shared and distributed-memory type. This gave us the possibility of performing large benchmark calculations, with a Full-CI space dimension up to almost ten billion of symmetry-adapted Slater determinants.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/7398589" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="669ad66f1a5d4c8e2cf7d69bcfc777e2" rel="nofollow" data-download="{"attachment_id":48501649,"asset_id":7398589,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/48501649/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="13090833" href="https://independent.academia.edu/EldaRossi">Elda Rossi</a><script data-card-contents-for-user="13090833" type="text/json">{"id":13090833,"first_name":"Elda","last_name":"Rossi","domain_name":"independent","page_name":"EldaRossi","display_name":"Elda Rossi","profile_url":"https://independent.academia.edu/EldaRossi?f_ri=14118","photo":"https://0.academia-photos.com/13090833/11472019/12795553/s65_elda.rossi.jpg_oh_979f713e6a322830266511f84118300b_oe_57321205"}</script></span></span></li><li class="js-paper-rank-work_7398589 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="7398589"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 7398589, container: ".js-paper-rank-work_7398589", }); 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$(".js-view-count[data-work-id=7398589]").text(description); $(".js-view-count-work_7398589").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_7398589").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="7398589"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="80414" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematical_Sciences">Mathematical Sciences</a>, <script data-card-contents-for-ri="80414" type="text/json">{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="118582" rel="nofollow" href="https://www.academia.edu/Documents/in/Physical_sciences">Physical sciences</a>, <script data-card-contents-for-ri="118582" type="text/json">{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="780669" rel="nofollow" href="https://www.academia.edu/Documents/in/Input_Output">Input Output</a><script data-card-contents-for-ri="780669" type="text/json">{"id":780669,"name":"Input Output","url":"https://www.academia.edu/Documents/in/Input_Output?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=7398589]'), work: {"id":7398589,"title":"A parallel Full-CI algorithm","created_at":"2014-06-19T05:14:30.170-07:00","url":"https://www.academia.edu/7398589/A_parallel_Full_CI_algorithm?f_ri=14118","dom_id":"work_7398589","summary":"A Full Configuration Interaction (Full-CI) algorithm is described. It is an integral-driven approach, with on-the-fly computation of the string-excitation lists that realize the application of the Hamiltonian to the Full-CI vector. The algorithm has been implemented on vector and parallel architectures, both of shared and distributed-memory type. This gave us the possibility of performing large benchmark calculations, with a Full-CI space dimension up to almost ten billion of symmetry-adapted Slater determinants.","downloadable_attachments":[{"id":48501649,"asset_id":7398589,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":13090833,"first_name":"Elda","last_name":"Rossi","domain_name":"independent","page_name":"EldaRossi","display_name":"Elda Rossi","profile_url":"https://independent.academia.edu/EldaRossi?f_ri=14118","photo":"https://0.academia-photos.com/13090833/11472019/12795553/s65_elda.rossi.jpg_oh_979f713e6a322830266511f84118300b_oe_57321205"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=14118","nofollow":true},{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences?f_ri=14118","nofollow":true},{"id":780669,"name":"Input Output","url":"https://www.academia.edu/Documents/in/Input_Output?f_ri=14118","nofollow":true},{"id":1745713,"name":"Parallel Architecture","url":"https://www.academia.edu/Documents/in/Parallel_Architecture?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_18977097" data-work_id="18977097" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/18977097/RFID_based_Distributed_Memory_for_Mobile_Applications">RFID-based Distributed Memory for Mobile Applications</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The goal of our work is to give a user equipped with an RFID-enabled mobile handset (mobile phone, PDA, laptop...) the abil- ity to know the contents of distant passive RFID tags, without physically moving to them and without using a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_18977097" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The goal of our work is to give a user equipped with an RFID-enabled mobile handset (mobile phone, PDA, laptop...) the abil- ity to know the contents of distant passive RFID tags, without physically moving to them and without using a Wireless Area Network. The existing architectural patterns involving passive tags do not meet simultaneously all of these requirements. Our</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/18977097" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ce3d450ef70e2a30fd2b2ad5f9bb20e9" rel="nofollow" data-download="{"attachment_id":42123376,"asset_id":18977097,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42123376/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="39033444" href="https://independent.academia.edu/MichelSimatic">Michel Simatic</a><script data-card-contents-for-user="39033444" type="text/json">{"id":39033444,"first_name":"Michel","last_name":"Simatic","domain_name":"independent","page_name":"MichelSimatic","display_name":"Michel Simatic","profile_url":"https://independent.academia.edu/MichelSimatic?f_ri=14118","photo":"https://0.academia-photos.com/39033444/10793290/12047069/s65_michel.simatic.jpg"}</script></span></span></li><li class="js-paper-rank-work_18977097 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="18977097"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 18977097, container: ".js-paper-rank-work_18977097", }); 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$(".js-view-count[data-work-id=18977097]").text(description); $(".js-view-count-work_18977097").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_18977097").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="18977097"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="55018" rel="nofollow" href="https://www.academia.edu/Documents/in/Mobile_Application">Mobile Application</a>, <script data-card-contents-for-ri="55018" type="text/json">{"id":55018,"name":"Mobile Application","url":"https://www.academia.edu/Documents/in/Mobile_Application?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="153168" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Collection">Data Collection</a>, <script data-card-contents-for-ri="153168" type="text/json">{"id":153168,"name":"Data Collection","url":"https://www.academia.edu/Documents/in/Data_Collection?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="217280" rel="nofollow" href="https://www.academia.edu/Documents/in/Mobile_phone">Mobile phone</a><script data-card-contents-for-ri="217280" type="text/json">{"id":217280,"name":"Mobile phone","url":"https://www.academia.edu/Documents/in/Mobile_phone?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=18977097]'), work: {"id":18977097,"title":"RFID-based Distributed Memory for Mobile Applications","created_at":"2015-11-25T03:42:04.658-08:00","url":"https://www.academia.edu/18977097/RFID_based_Distributed_Memory_for_Mobile_Applications?f_ri=14118","dom_id":"work_18977097","summary":"The goal of our work is to give a user equipped with an RFID-enabled mobile handset (mobile phone, PDA, laptop...) the abil- ity to know the contents of distant passive RFID tags, without physically moving to them and without using a Wireless Area Network. The existing architectural patterns involving passive tags do not meet simultaneously all of these requirements. Our","downloadable_attachments":[{"id":42123376,"asset_id":18977097,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":39033444,"first_name":"Michel","last_name":"Simatic","domain_name":"independent","page_name":"MichelSimatic","display_name":"Michel Simatic","profile_url":"https://independent.academia.edu/MichelSimatic?f_ri=14118","photo":"https://0.academia-photos.com/39033444/10793290/12047069/s65_michel.simatic.jpg"}],"research_interests":[{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":55018,"name":"Mobile Application","url":"https://www.academia.edu/Documents/in/Mobile_Application?f_ri=14118","nofollow":true},{"id":153168,"name":"Data Collection","url":"https://www.academia.edu/Documents/in/Data_Collection?f_ri=14118","nofollow":true},{"id":217280,"name":"Mobile phone","url":"https://www.academia.edu/Documents/in/Mobile_phone?f_ri=14118","nofollow":true},{"id":2070031,"name":"Rfid Tag","url":"https://www.academia.edu/Documents/in/Rfid_Tag?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_67198436" data-work_id="67198436" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/67198436/Parallel_solution_of_three_dimensional_Marangoni_flow_in_liquid_bridges">Parallel solution of three-dimensional Marangoni flow in liquid bridges</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper describes the implementation and performances of a parallel solver for the direct numerical simulation of the three-dimensional and time-dependent Navier Stokes equations on distributed-memory, massively parallel computers. The... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_67198436" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper describes the implementation and performances of a parallel solver for the direct numerical simulation of the three-dimensional and time-dependent Navier Stokes equations on distributed-memory, massively parallel computers. The feasibility of this approach to study Marangoni flow instability in half zone liquid bridges is examined. The results indicate that the incompressible, non linear Navier-Stokes problem, governing the Marangoni flows behaviour, can effectively be parallelized on a distributedmemory parallel machine by remapping the distributed data structure. The numerical code is based on a three-dimensional Simplified Marker and Cell primitive variable method applied to a staggered finitedifference grid. Using this method, the problem is split in two problems, one parabolic and the other elliptic. A parallel algorithm, explicit in time, is utilized to solve the parabolic equations. A parallel multisplitting kernel is introduced for the solution of the pseudo-pressure elliptic equation, representing the most time-consuming part of the algorithm. A grid-partition strategy is used in the parallel implementations of both the parabolic equations and the multisplitting elliptic kernel. A Message Passing Interface (MPI) is coded for the boundary conditions; this protocol is portable to different systems supporting this interface for interprocessor communications. Numerical experiments illustrate good numerical properties and parallel efficiency. In particular, good scalability on a large number of processors can be achieved as long as the granularity of the parallel application is not too small. However, increasing the number of processors, the Speed-Up is ever smaller than the ideal linear Speed Up. The communication timings indicate that complex practical calculations, such as the solutions of the Navier-Stokes equations for the numerical simulation of the instability of Marangoni flows, can be expected to run on a massively parallel machine with good efficiency.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/67198436" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e7cb706ee0e1a1eb39a53ce98fb765ff" rel="nofollow" data-download="{"attachment_id":78106136,"asset_id":67198436,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/78106136/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="30275990" href="https://unina.academia.edu/RaffaeleSavino">Raffaele Savino</a><script data-card-contents-for-user="30275990" type="text/json">{"id":30275990,"first_name":"Raffaele","last_name":"Savino","domain_name":"unina","page_name":"RaffaeleSavino","display_name":"Raffaele Savino","profile_url":"https://unina.academia.edu/RaffaeleSavino?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_67198436 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="67198436"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 67198436, container: ".js-paper-rank-work_67198436", }); 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$(".js-view-count[data-work-id=67198436]").text(description); $(".js-view-count-work_67198436").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_67198436").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="67198436"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">18</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="48" rel="nofollow" href="https://www.academia.edu/Documents/in/Engineering">Engineering</a>, <script data-card-contents-for-ri="48" type="text/json">{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10719" rel="nofollow" href="https://www.academia.edu/Documents/in/Direct_Numerical_Simulation">Direct Numerical Simulation</a>, <script data-card-contents-for-ri="10719" type="text/json">{"id":10719,"name":"Direct Numerical Simulation","url":"https://www.academia.edu/Documents/in/Direct_Numerical_Simulation?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="16496" rel="nofollow" href="https://www.academia.edu/Documents/in/Fluid_Dynamics">Fluid Dynamics</a><script data-card-contents-for-ri="16496" type="text/json">{"id":16496,"name":"Fluid Dynamics","url":"https://www.academia.edu/Documents/in/Fluid_Dynamics?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=67198436]'), work: {"id":67198436,"title":"Parallel solution of three-dimensional Marangoni flow in liquid bridges","created_at":"2022-01-05T01:27:44.059-08:00","url":"https://www.academia.edu/67198436/Parallel_solution_of_three_dimensional_Marangoni_flow_in_liquid_bridges?f_ri=14118","dom_id":"work_67198436","summary":"This paper describes the implementation and performances of a parallel solver for the direct numerical simulation of the three-dimensional and time-dependent Navier Stokes equations on distributed-memory, massively parallel computers. The feasibility of this approach to study Marangoni flow instability in half zone liquid bridges is examined. The results indicate that the incompressible, non linear Navier-Stokes problem, governing the Marangoni flows behaviour, can effectively be parallelized on a distributedmemory parallel machine by remapping the distributed data structure. The numerical code is based on a three-dimensional Simplified Marker and Cell primitive variable method applied to a staggered finitedifference grid. Using this method, the problem is split in two problems, one parabolic and the other elliptic. A parallel algorithm, explicit in time, is utilized to solve the parabolic equations. A parallel multisplitting kernel is introduced for the solution of the pseudo-pressure elliptic equation, representing the most time-consuming part of the algorithm. A grid-partition strategy is used in the parallel implementations of both the parabolic equations and the multisplitting elliptic kernel. A Message Passing Interface (MPI) is coded for the boundary conditions; this protocol is portable to different systems supporting this interface for interprocessor communications. Numerical experiments illustrate good numerical properties and parallel efficiency. In particular, good scalability on a large number of processors can be achieved as long as the granularity of the parallel application is not too small. However, increasing the number of processors, the Speed-Up is ever smaller than the ideal linear Speed Up. The communication timings indicate that complex practical calculations, such as the solutions of the Navier-Stokes equations for the numerical simulation of the instability of Marangoni flows, can be expected to run on a massively parallel machine with good efficiency.","downloadable_attachments":[{"id":78106136,"asset_id":67198436,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":30275990,"first_name":"Raffaele","last_name":"Savino","domain_name":"unina","page_name":"RaffaeleSavino","display_name":"Raffaele Savino","profile_url":"https://unina.academia.edu/RaffaeleSavino?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=14118","nofollow":true},{"id":10719,"name":"Direct Numerical Simulation","url":"https://www.academia.edu/Documents/in/Direct_Numerical_Simulation?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":16496,"name":"Fluid Dynamics","url":"https://www.academia.edu/Documents/in/Fluid_Dynamics?f_ri=14118","nofollow":true},{"id":60658,"name":"Numerical Simulation","url":"https://www.academia.edu/Documents/in/Numerical_Simulation?f_ri=14118"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences?f_ri=14118"},{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences?f_ri=14118"},{"id":225293,"name":"Navier Stokes","url":"https://www.academia.edu/Documents/in/Navier_Stokes?f_ri=14118"},{"id":233179,"name":"Parabolic Wave Equation","url":"https://www.academia.edu/Documents/in/Parabolic_Wave_Equation?f_ri=14118"},{"id":348986,"name":"Parallel Algorithm","url":"https://www.academia.edu/Documents/in/Parallel_Algorithm?f_ri=14118"},{"id":394477,"name":"Time Dependent","url":"https://www.academia.edu/Documents/in/Time_Dependent?f_ri=14118"},{"id":452692,"name":"Finite Difference","url":"https://www.academia.edu/Documents/in/Finite_Difference?f_ri=14118"},{"id":504035,"name":"Three Dimensional","url":"https://www.academia.edu/Documents/in/Three_Dimensional?f_ri=14118"},{"id":507975,"name":"Parallel Machines","url":"https://www.academia.edu/Documents/in/Parallel_Machines?f_ri=14118"},{"id":702169,"name":"Parallel Computer","url":"https://www.academia.edu/Documents/in/Parallel_Computer?f_ri=14118"},{"id":733999,"name":"Message Passing Interface","url":"https://www.academia.edu/Documents/in/Message_Passing_Interface?f_ri=14118"},{"id":867022,"name":"Boundary Condition","url":"https://www.academia.edu/Documents/in/Boundary_Condition?f_ri=14118"},{"id":2660047,"name":"Elliptic equation","url":"https://www.academia.edu/Documents/in/Elliptic_equation?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_890820" data-work_id="890820" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/890820/Parallel_strategies_for_the_local_biological_sequence_alignment_in_a_cluster_of_workstations">Parallel strategies for the local biological sequence alignment in a cluster of workstations</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Recently, many organisms have had their DNA entirely sequenced. This reality presents the need for comparing long DNA sequences, which is a challenging task due to its high demands for computational power and memory. Sequence comparison... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_890820" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Recently, many organisms have had their DNA entirely sequenced. This reality presents the need for comparing long DNA sequences, which is a challenging task due to its high demands for computational power and memory. Sequence comparison is a basic operation in DNA sequencing projects, and most sequence comparison methods currently in use are based on heuristics, which are faster but offer no guarantees of producing the best alignments possible. In order to alleviate this problem, Smith-Waterman proposed an algorithm. This algorithm obtains the best local alignments but at the expense of very high computing power and huge memory requirements. In this article, we present and evaluate our experiments involving three strategies to run the Smith-Waterman algorithm in a cluster of workstations using a Distributed Shared Memory System. Our results on an eight-machine cluster presented very good speed-up and indicate that impressive improvements can be achieved depending on the strategy used. In addition, we present a number of theoretical remarks concerning how to reduce the amount of memory used. (A. Boukerche), <a href="mailto:albamm@cic.unb.br" rel="nofollow">albamm@cic.unb.br</a> (A.C.M.A. de Melo), <a href="mailto:ayala@mat.unb.br" rel="nofollow">ayala@mat.unb.br</a> (M. Ayala-Rincón), <a href="mailto:mia@cic.unb.br" rel="nofollow">mia@cic.unb.br</a> (M.E.M.T. Walter). 0743-7315/$ -see front matter</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/890820" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4b282a2ef592858e81df762fabe506de" rel="nofollow" data-download="{"attachment_id":51179056,"asset_id":890820,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/51179056/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="712013" href="https://unb.academia.edu/AlbaCristinaMagalhaesAlvesdeMelo">Alba Cristina Magalhaes Alves de Melo</a><script data-card-contents-for-user="712013" type="text/json">{"id":712013,"first_name":"Alba Cristina","last_name":"Magalhaes Alves de Melo","domain_name":"unb","page_name":"AlbaCristinaMagalhaesAlvesdeMelo","display_name":"Alba Cristina Magalhaes Alves de Melo","profile_url":"https://unb.academia.edu/AlbaCristinaMagalhaesAlvesdeMelo?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_890820 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="890820"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 890820, container: ".js-paper-rank-work_890820", }); 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$(".js-view-count[data-work-id=890820]").text(description); $(".js-view-count-work_890820").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_890820").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="890820"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="440" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Computing">Distributed Computing</a>, <script data-card-contents-for-ri="440" type="text/json">{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14118" rel="nofollow" href="https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System">Distributed Shared Memory System</a>, <script data-card-contents-for-ri="14118" type="text/json">{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="34740" rel="nofollow" href="https://www.academia.edu/Documents/in/Cluster_Computing">Cluster Computing</a>, <script data-card-contents-for-ri="34740" type="text/json">{"id":34740,"name":"Cluster Computing","url":"https://www.academia.edu/Documents/in/Cluster_Computing?f_ri=14118","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="67484" rel="nofollow" href="https://www.academia.edu/Documents/in/Sequence_alignment">Sequence alignment</a><script data-card-contents-for-ri="67484" type="text/json">{"id":67484,"name":"Sequence alignment","url":"https://www.academia.edu/Documents/in/Sequence_alignment?f_ri=14118","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=890820]'), work: {"id":890820,"title":"Parallel strategies for the local biological sequence alignment in a cluster of workstations","created_at":"2011-09-06T04:38:37.844-07:00","url":"https://www.academia.edu/890820/Parallel_strategies_for_the_local_biological_sequence_alignment_in_a_cluster_of_workstations?f_ri=14118","dom_id":"work_890820","summary":"Recently, many organisms have had their DNA entirely sequenced. This reality presents the need for comparing long DNA sequences, which is a challenging task due to its high demands for computational power and memory. Sequence comparison is a basic operation in DNA sequencing projects, and most sequence comparison methods currently in use are based on heuristics, which are faster but offer no guarantees of producing the best alignments possible. In order to alleviate this problem, Smith-Waterman proposed an algorithm. This algorithm obtains the best local alignments but at the expense of very high computing power and huge memory requirements. In this article, we present and evaluate our experiments involving three strategies to run the Smith-Waterman algorithm in a cluster of workstations using a Distributed Shared Memory System. Our results on an eight-machine cluster presented very good speed-up and indicate that impressive improvements can be achieved depending on the strategy used. In addition, we present a number of theoretical remarks concerning how to reduce the amount of memory used. (A. Boukerche), albamm@cic.unb.br (A.C.M.A. de Melo), ayala@mat.unb.br (M. Ayala-Rincón), mia@cic.unb.br (M.E.M.T. Walter). 0743-7315/$ -see front matter","downloadable_attachments":[{"id":51179056,"asset_id":890820,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":712013,"first_name":"Alba Cristina","last_name":"Magalhaes Alves de Melo","domain_name":"unb","page_name":"AlbaCristinaMagalhaesAlvesdeMelo","display_name":"Alba Cristina Magalhaes Alves de Melo","profile_url":"https://unb.academia.edu/AlbaCristinaMagalhaesAlvesdeMelo?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":34740,"name":"Cluster Computing","url":"https://www.academia.edu/Documents/in/Cluster_Computing?f_ri=14118","nofollow":true},{"id":67484,"name":"Sequence alignment","url":"https://www.academia.edu/Documents/in/Sequence_alignment?f_ri=14118","nofollow":true},{"id":80870,"name":"Parallel \u0026 Distributed Computing","url":"https://www.academia.edu/Documents/in/Parallel_and_Distributed_Computing?f_ri=14118"},{"id":230429,"name":"Parallel","url":"https://www.academia.edu/Documents/in/Parallel?f_ri=14118"},{"id":310719,"name":"Local Alignment","url":"https://www.academia.edu/Documents/in/Local_Alignment?f_ri=14118"},{"id":348986,"name":"Parallel Algorithm","url":"https://www.academia.edu/Documents/in/Parallel_Algorithm?f_ri=14118"},{"id":2274872,"name":"DNA sequence","url":"https://www.academia.edu/Documents/in/DNA_sequence?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_10619199" data-work_id="10619199" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/10619199/EpiFast">EpiFast</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Large scale realistic epidemic simulations have recently become an increasingly important application of high-performance computing. We propose a parallel algorithm, EpiFast, based on a novel interpretation of the stochastic disease... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_10619199" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Large scale realistic epidemic simulations have recently become an increasingly important application of high-performance computing. We propose a parallel algorithm, EpiFast, based on a novel interpretation of the stochastic disease propagation in a contact network. We implement it using a masterslave computation model which allows scalability on distributed memory systems.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/10619199" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="09bf8600a8f43fbe1e125d3b508a7423" rel="nofollow" data-download="{"attachment_id":47258687,"asset_id":10619199,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/47258687/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="25967037" href="https://independent.academia.edu/XizhouFeng">Xizhou Feng</a><script data-card-contents-for-user="25967037" type="text/json">{"id":25967037,"first_name":"Xizhou","last_name":"Feng","domain_name":"independent","page_name":"XizhouFeng","display_name":"Xizhou Feng","profile_url":"https://independent.academia.edu/XizhouFeng?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_10619199 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="10619199"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 10619199, container: ".js-paper-rank-work_10619199", }); 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We propose a parallel algorithm, EpiFast, based on a novel interpretation of the stochastic disease propagation in a contact network. We implement it using a masterslave computation model which allows scalability on distributed memory systems.","downloadable_attachments":[{"id":47258687,"asset_id":10619199,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":25967037,"first_name":"Xizhou","last_name":"Feng","domain_name":"independent","page_name":"XizhouFeng","display_name":"Xizhou Feng","profile_url":"https://independent.academia.edu/XizhouFeng?f_ri=14118","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":6810,"name":"Public Health Policy","url":"https://www.academia.edu/Documents/in/Public_Health_Policy?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118","nofollow":true},{"id":48636,"name":"Simulation","url":"https://www.academia.edu/Documents/in/Simulation?f_ri=14118","nofollow":true},{"id":85294,"name":"Computer Model","url":"https://www.academia.edu/Documents/in/Computer_Model?f_ri=14118","nofollow":true},{"id":97733,"name":"Shared memory","url":"https://www.academia.edu/Documents/in/Shared_memory?f_ri=14118"},{"id":149081,"name":"Decision Support","url":"https://www.academia.edu/Documents/in/Decision_Support?f_ri=14118"},{"id":348986,"name":"Parallel Algorithm","url":"https://www.academia.edu/Documents/in/Parallel_Algorithm?f_ri=14118"},{"id":758278,"name":"Large Scale","url":"https://www.academia.edu/Documents/in/Large_Scale?f_ri=14118"},{"id":850706,"name":"High performance computer","url":"https://www.academia.edu/Documents/in/High_performance_computer?f_ri=14118"},{"id":1181607,"name":"Simulation Tool","url":"https://www.academia.edu/Documents/in/Simulation_Tool?f_ri=14118"},{"id":1957725,"name":"Fast Algorithm","url":"https://www.academia.edu/Documents/in/Fast_Algorithm?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_1008906" data-work_id="1008906" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/1008906/A_Scalable_Multi_Thread_Multi_Issue_Array_Processor_Architecture_for_DSP_Applications_Based_on_Extended_Tomasulo_Scheme">A Scalable, Multi-Thread, Multi-Issue Array Processor Architecture for DSP Applications Based on Extended Tomasulo Scheme</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A scalable, distributed micro-architecture is presented that emphasizes on high performance computing for digital signal processing applications by combining high frequency design techniques with a very high degree of parallel processing... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_1008906" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A scalable, distributed micro-architecture is presented that emphasizes on high performance computing for digital signal processing applications by combining high frequency design techniques with a very high degree of parallel processing on a chip. The architecture is based on a superscalar processor model with out-of-order execution, that supports specialized, complex DSP function units, and simultaneous instruction issue from multiple independent threads (SMT). Consequent application of fine clustering reduces the cycle-time for wiresensitive building blocks of the processor like the register file and leads to a distributed architecture model, where independent thread processing units, ALUs, registers files and memories are distributed across the chip and communicate with each other by special networks, forming a "network-on-a-chip" (NOC) [1]. The communication protocol is a modified version of Tomasulo's scheme , that was extended to eliminate all central control structures for the data flow and to support multithreading. The performance of the architecture is scalable with both the number of function units and the number of thread units without having any impact on the processors cycle-time.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/1008906" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b84240e8b692f3998baed6e911937d62" rel="nofollow" data-download="{"attachment_id":6172370,"asset_id":1008906,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/6172370/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="258168" href="https://tu-braunschweig.academia.edu/MladenBerekovic">Mladen Berekovic</a><script data-card-contents-for-user="258168" type="text/json">{"id":258168,"first_name":"Mladen","last_name":"Berekovic","domain_name":"tu-braunschweig","page_name":"MladenBerekovic","display_name":"Mladen Berekovic","profile_url":"https://tu-braunschweig.academia.edu/MladenBerekovic?f_ri=14118","photo":"https://0.academia-photos.com/258168/19083352/19033178/s65_mladen.berekovic.jpg"}</script></span></span></li><li class="js-paper-rank-work_1008906 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="1008906"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 1008906, container: ".js-paper-rank-work_1008906", }); 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The architecture is based on a superscalar processor model with out-of-order execution, that supports specialized, complex DSP function units, and simultaneous instruction issue from multiple independent threads (SMT). Consequent application of fine clustering reduces the cycle-time for wiresensitive building blocks of the processor like the register file and leads to a distributed architecture model, where independent thread processing units, ALUs, registers files and memories are distributed across the chip and communicate with each other by special networks, forming a \"network-on-a-chip\" (NOC) [1]. The communication protocol is a modified version of Tomasulo's scheme , that was extended to eliminate all central control structures for the data flow and to support multithreading. The performance of the architecture is scalable with both the number of function units and the number of thread units without having any impact on the processors cycle-time.","downloadable_attachments":[{"id":6172370,"asset_id":1008906,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":258168,"first_name":"Mladen","last_name":"Berekovic","domain_name":"tu-braunschweig","page_name":"MladenBerekovic","display_name":"Mladen Berekovic","profile_url":"https://tu-braunschweig.academia.edu/MladenBerekovic?f_ri=14118","photo":"https://0.academia-photos.com/258168/19083352/19033178/s65_mladen.berekovic.jpg"}],"research_interests":[{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing?f_ri=14118","nofollow":true},{"id":2141,"name":"Signal Processing","url":"https://www.academia.edu/Documents/in/Signal_Processing?f_ri=14118","nofollow":true},{"id":6177,"name":"Modeling","url":"https://www.academia.edu/Documents/in/Modeling?f_ri=14118","nofollow":true},{"id":9038,"name":"Digital Signal Processing","url":"https://www.academia.edu/Documents/in/Digital_Signal_Processing?f_ri=14118","nofollow":true},{"id":14118,"name":"Distributed Shared Memory System","url":"https://www.academia.edu/Documents/in/Distributed_Shared_Memory_System?f_ri=14118"},{"id":17167,"name":"Parallel Processing","url":"https://www.academia.edu/Documents/in/Parallel_Processing?f_ri=14118"},{"id":22686,"name":"Distributed System","url":"https://www.academia.edu/Documents/in/Distributed_System?f_ri=14118"},{"id":48458,"name":"High Frequency","url":"https://www.academia.edu/Documents/in/High_Frequency?f_ri=14118"},{"id":53994,"name":"Data Structure","url":"https://www.academia.edu/Documents/in/Data_Structure?f_ri=14118"},{"id":59332,"name":"Processor Architecture","url":"https://www.academia.edu/Documents/in/Processor_Architecture?f_ri=14118"},{"id":70648,"name":"Concurrency","url":"https://www.academia.edu/Documents/in/Concurrency?f_ri=14118"},{"id":96207,"name":"Microarchitecture","url":"https://www.academia.edu/Documents/in/Microarchitecture?f_ri=14118"},{"id":106145,"name":"Classification","url":"https://www.academia.edu/Documents/in/Classification?f_ri=14118"},{"id":109799,"name":"Multiprocessor Scheduling","url":"https://www.academia.edu/Documents/in/Multiprocessor_Scheduling?f_ri=14118"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis?f_ri=14118"},{"id":302693,"name":"Cycle Time","url":"https://www.academia.edu/Documents/in/Cycle_Time?f_ri=14118"},{"id":322954,"name":"Chip","url":"https://www.academia.edu/Documents/in/Chip?f_ri=14118"},{"id":377043,"name":"Scalability","url":"https://www.academia.edu/Documents/in/Scalability?f_ri=14118"},{"id":408558,"name":"Control Structure","url":"https://www.academia.edu/Documents/in/Control_Structure?f_ri=14118"},{"id":432786,"name":"System on a Chip","url":"https://www.academia.edu/Documents/in/System_on_a_Chip?f_ri=14118"},{"id":476526,"name":"Data Flow Diagram","url":"https://www.academia.edu/Documents/in/Data_Flow_Diagram?f_ri=14118"},{"id":546617,"name":"Communication Protocol","url":"https://www.academia.edu/Documents/in/Communication_Protocol?f_ri=14118"},{"id":850706,"name":"High performance computer","url":"https://www.academia.edu/Documents/in/High_performance_computer?f_ri=14118"},{"id":1998943,"name":"Register File","url":"https://www.academia.edu/Documents/in/Register_File?f_ri=14118"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_13213758" data-work_id="13213758" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/13213758/MigThread_Thread_migration_in_DSM_systems">MigThread: Thread migration in DSM systems</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Distributed Shared Memory (DSM) systems provide a logically shared memory over physically distributed memory to enable parallel computation on Networks of Workstations (NOWs). In this paper, we propose an infrastructure for DSM systems to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_13213758" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Distributed Shared Memory (DSM) systems provide a logically shared memory over physically distributed memory to enable parallel computation on Networks of Workstations (NOWs). In this paper, we propose an infrastructure for DSM systems to utilize idle cycles in the network by thread migration.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/13213758" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="59de689a0284d7040430b28d04a0bc44" rel="nofollow" data-download="{"attachment_id":45586305,"asset_id":13213758,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/45586305/download_file?st=MTczOTcxNDI2MCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32465998" href="https://independent.academia.edu/VipinChaudhary5">Vipin Chaudhary</a><script data-card-contents-for-user="32465998" type="text/json">{"id":32465998,"first_name":"Vipin","last_name":"Chaudhary","domain_name":"independent","page_name":"VipinChaudhary5","display_name":"Vipin Chaudhary","profile_url":"https://independent.academia.edu/VipinChaudhary5?f_ri=14118","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_13213758 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="13213758"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 13213758, container: ".js-paper-rank-work_13213758", }); 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