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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.04633">arXiv:2301.04633</a> <span> [<a href="https://arxiv.org/pdf/2301.04633">pdf</a>, <a href="https://arxiv.org/ps/2301.04633">ps</a>, <a href="https://arxiv.org/format/2301.04633">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/s41781-023-00101-0">10.1007/s41781-023-00101-0 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cai%2C+T">Tejin Cai</a>, <a href="/search/cs?searchtype=author&query=Herner%2C+K">Kenneth Herner</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+T">Tingjun Yang</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+M">Michael Wang</a>, <a href="/search/cs?searchtype=author&query=Flechas%2C+M+A">Maria Acosta Flechas</a>, <a href="/search/cs?searchtype=author&query=Harris%2C+P">Philip Harris</a>, <a href="/search/cs?searchtype=author&query=Holzman%2C+B">Burt Holzman</a>, <a href="/search/cs?searchtype=author&query=Pedro%2C+K">Kevin Pedro</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+N">Nhan Tran</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.04633v2-abstract-short" style="display: inline;"> We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission tools, we attempt to reprocess the data by running several thousand concurrent grid jobs, a rate we expect to be typical of current and future neutrino physics e… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.04633v2-abstract-full').style.display = 'inline'; document.getElementById('2301.04633v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.04633v2-abstract-full" style="display: none;"> We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission tools, we attempt to reprocess the data by running several thousand concurrent grid jobs, a rate we expect to be typical of current and future neutrino physics experiments. We process most of the dataset with the GPU version of our processing algorithm and the remainder with the CPU version for timing comparisons. We find that a 100-GPU cloud-based server is able to easily meet the processing demand, and that using the GPU version of the event processing algorithm is two times faster than processing these data with the CPU version when comparing to the newest CPUs in our sample. The amount of data transferred to the inference server during the GPU runs can overwhelm even the highest-bandwidth network switches, however, unless care is taken to observe network facility limits or otherwise distribute the jobs to multiple sites. We discuss the lessons learned from this processing campaign and several avenues for future improvements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.04633v2-abstract-full').style.display = 'none'; document.getElementById('2301.04633v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 9 figures, matches accepted version</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-PUB-22-944-ND-PPD-SCD </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Comput Softw Big Sci 7, 11 (2023) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.16255">arXiv:2203.16255</a> <span> [<a href="https://arxiv.org/pdf/2203.16255">pdf</a>, <a href="https://arxiv.org/format/2203.16255">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="General Relativity and Quantum Cosmology">gr-qc</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> </div> <p class="title is-5 mathjax"> Physics Community Needs, Tools, and Resources for Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Harris%2C+P">Philip Harris</a>, <a href="/search/cs?searchtype=author&query=Katsavounidis%2C+E">Erik Katsavounidis</a>, <a href="/search/cs?searchtype=author&query=McCormack%2C+W+P">William Patrick McCormack</a>, <a href="/search/cs?searchtype=author&query=Rankin%2C+D">Dylan Rankin</a>, <a href="/search/cs?searchtype=author&query=Feng%2C+Y">Yongbin Feng</a>, <a href="/search/cs?searchtype=author&query=Gandrakota%2C+A">Abhijith Gandrakota</a>, <a href="/search/cs?searchtype=author&query=Herwig%2C+C">Christian Herwig</a>, <a href="/search/cs?searchtype=author&query=Holzman%2C+B">Burt Holzman</a>, <a href="/search/cs?searchtype=author&query=Pedro%2C+K">Kevin Pedro</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+N">Nhan Tran</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+T">Tingjun Yang</a>, <a href="/search/cs?searchtype=author&query=Ngadiuba%2C+J">Jennifer Ngadiuba</a>, <a href="/search/cs?searchtype=author&query=Coughlin%2C+M">Michael Coughlin</a>, <a href="/search/cs?searchtype=author&query=Hauck%2C+S">Scott Hauck</a>, <a href="/search/cs?searchtype=author&query=Hsu%2C+S">Shih-Chieh Hsu</a>, <a href="/search/cs?searchtype=author&query=Khoda%2C+E+E">Elham E Khoda</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+D">Deming Chen</a>, <a href="/search/cs?searchtype=author&query=Neubauer%2C+M">Mark Neubauer</a>, <a href="/search/cs?searchtype=author&query=Duarte%2C+J">Javier Duarte</a>, <a href="/search/cs?searchtype=author&query=Karagiorgi%2C+G">Georgia Karagiorgi</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+M">Mia Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.16255v1-abstract-short" style="display: inline;"> Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utiliz… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.16255v1-abstract-full').style.display = 'inline'; document.getElementById('2203.16255v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.16255v1-abstract-full" style="display: none;"> Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utilized and accessed in the coming years. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.16255v1-abstract-full').style.display = 'none'; document.getElementById('2203.16255v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Contribution to Snowmass 2021, 33 pages, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.10161">arXiv:2203.10161</a> <span> [<a href="https://arxiv.org/pdf/2203.10161">pdf</a>, <a href="https://arxiv.org/format/2203.10161">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Collaborative Computing Support for Analysis Facilities Exploiting Software as Infrastructure Techniques </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Flechas%2C+M+A">Maria Acosta Flechas</a>, <a href="/search/cs?searchtype=author&query=Attebury%2C+G">Garhan Attebury</a>, <a href="/search/cs?searchtype=author&query=Bloom%2C+K">Kenneth Bloom</a>, <a href="/search/cs?searchtype=author&query=Bockelman%2C+B">Brian Bockelman</a>, <a href="/search/cs?searchtype=author&query=Gray%2C+L">Lindsey Gray</a>, <a href="/search/cs?searchtype=author&query=Holzman%2C+B">Burt Holzman</a>, <a href="/search/cs?searchtype=author&query=Lundstedt%2C+C">Carl Lundstedt</a>, <a href="/search/cs?searchtype=author&query=Shadura%2C+O">Oksana Shadura</a>, <a href="/search/cs?searchtype=author&query=Smith%2C+N">Nicholas Smith</a>, <a href="/search/cs?searchtype=author&query=Thiltges%2C+J">John Thiltges</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.10161v2-abstract-short" style="display: inline;"> Prior to the public release of Kubernetes it was difficult to conduct joint development of elaborate analysis facilities due to the highly non-homogeneous nature of hardware and network topology across compute facilities. However, since the advent of systems like Kubernetes and OpenShift, which provide declarative interfaces for building fault-tolerant and self-healing deployments of networked sof… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.10161v2-abstract-full').style.display = 'inline'; document.getElementById('2203.10161v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.10161v2-abstract-full" style="display: none;"> Prior to the public release of Kubernetes it was difficult to conduct joint development of elaborate analysis facilities due to the highly non-homogeneous nature of hardware and network topology across compute facilities. However, since the advent of systems like Kubernetes and OpenShift, which provide declarative interfaces for building fault-tolerant and self-healing deployments of networked software, it is possible for multiple institutes to collaborate more effectively since resource details are abstracted away through various forms of hardware and software virtualization. In this whitepaper we will outline the development of two analysis facilities: "Coffea-casa" at University of Nebraska Lincoln and the "Elastic Analysis Facility" at Fermilab, and how utilizing platform abstraction has improved the development of common software for each of these facilities, and future development plans made possible by this methodology. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.10161v2-abstract-full').style.display = 'none'; document.getElementById('2203.10161v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">contribution to Snowmass 2021</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-FN-1163-SCD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.13041">arXiv:2110.13041</a> <span> [<a href="https://arxiv.org/pdf/2110.13041">pdf</a>, <a href="https://arxiv.org/format/2110.13041">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3389/fdata.2022.787421">10.3389/fdata.2022.787421 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Applications and Techniques for Fast Machine Learning in Science </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Deiana%2C+A+M">Allison McCarn Deiana</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+N">Nhan Tran</a>, <a href="/search/cs?searchtype=author&query=Agar%2C+J">Joshua Agar</a>, <a href="/search/cs?searchtype=author&query=Blott%2C+M">Michaela Blott</a>, <a href="/search/cs?searchtype=author&query=Di+Guglielmo%2C+G">Giuseppe Di Guglielmo</a>, <a href="/search/cs?searchtype=author&query=Duarte%2C+J">Javier Duarte</a>, <a href="/search/cs?searchtype=author&query=Harris%2C+P">Philip Harris</a>, <a href="/search/cs?searchtype=author&query=Hauck%2C+S">Scott Hauck</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+M">Mia Liu</a>, <a href="/search/cs?searchtype=author&query=Neubauer%2C+M+S">Mark S. Neubauer</a>, <a href="/search/cs?searchtype=author&query=Ngadiuba%2C+J">Jennifer Ngadiuba</a>, <a href="/search/cs?searchtype=author&query=Ogrenci-Memik%2C+S">Seda Ogrenci-Memik</a>, <a href="/search/cs?searchtype=author&query=Pierini%2C+M">Maurizio Pierini</a>, <a href="/search/cs?searchtype=author&query=Aarrestad%2C+T">Thea Aarrestad</a>, <a href="/search/cs?searchtype=author&query=Bahr%2C+S">Steffen Bahr</a>, <a href="/search/cs?searchtype=author&query=Becker%2C+J">Jurgen Becker</a>, <a href="/search/cs?searchtype=author&query=Berthold%2C+A">Anne-Sophie Berthold</a>, <a href="/search/cs?searchtype=author&query=Bonventre%2C+R+J">Richard J. Bonventre</a>, <a href="/search/cs?searchtype=author&query=Bravo%2C+T+E+M">Tomas E. Muller Bravo</a>, <a href="/search/cs?searchtype=author&query=Diefenthaler%2C+M">Markus Diefenthaler</a>, <a href="/search/cs?searchtype=author&query=Dong%2C+Z">Zhen Dong</a>, <a href="/search/cs?searchtype=author&query=Fritzsche%2C+N">Nick Fritzsche</a>, <a href="/search/cs?searchtype=author&query=Gholami%2C+A">Amir Gholami</a>, <a href="/search/cs?searchtype=author&query=Govorkova%2C+E">Ekaterina Govorkova</a>, <a href="/search/cs?searchtype=author&query=Hazelwood%2C+K+J">Kyle J Hazelwood</a> , et al. (62 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2110.13041v1-abstract-short" style="display: inline;"> In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML ac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.13041v1-abstract-full').style.display = 'inline'; document.getElementById('2110.13041v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.13041v1-abstract-full" style="display: none;"> In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.13041v1-abstract-full').style.display = 'none'; document.getElementById('2110.13041v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">66 pages, 13 figures, 5 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-PUB-21-502-AD-E-SCD </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Front. Big Data 5, 787421 (2022) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.08556">arXiv:2010.08556</a> <span> [<a href="https://arxiv.org/pdf/2010.08556">pdf</a>, <a href="https://arxiv.org/format/2010.08556">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/H2RC51942.2020.00010">10.1109/H2RC51942.2020.00010 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> FPGAs-as-a-Service Toolkit (FaaST) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rankin%2C+D+S">Dylan Sheldon Rankin</a>, <a href="/search/cs?searchtype=author&query=Krupa%2C+J">Jeffrey Krupa</a>, <a href="/search/cs?searchtype=author&query=Harris%2C+P">Philip Harris</a>, <a href="/search/cs?searchtype=author&query=Flechas%2C+M+A">Maria Acosta Flechas</a>, <a href="/search/cs?searchtype=author&query=Holzman%2C+B">Burt Holzman</a>, <a href="/search/cs?searchtype=author&query=Klijnsma%2C+T">Thomas Klijnsma</a>, <a href="/search/cs?searchtype=author&query=Pedro%2C+K">Kevin Pedro</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+N">Nhan Tran</a>, <a href="/search/cs?searchtype=author&query=Hauck%2C+S">Scott Hauck</a>, <a href="/search/cs?searchtype=author&query=Hsu%2C+S">Shih-Chieh Hsu</a>, <a href="/search/cs?searchtype=author&query=Trahms%2C+M">Matthew Trahms</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+K">Kelvin Lin</a>, <a href="/search/cs?searchtype=author&query=Lou%2C+Y">Yu Lou</a>, <a href="/search/cs?searchtype=author&query=Ho%2C+T">Ta-Wei Ho</a>, <a href="/search/cs?searchtype=author&query=Duarte%2C+J">Javier Duarte</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+M">Mia Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2010.08556v1-abstract-short" style="display: inline;"> Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically as-a-service computing, has the potential for significant gains over traditional computing models. Although previous studies and packages in the field of heterogeneous computing have focused on GPUs as accelerators, FPGAs… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.08556v1-abstract-full').style.display = 'inline'; document.getElementById('2010.08556v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.08556v1-abstract-full" style="display: none;"> Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically as-a-service computing, has the potential for significant gains over traditional computing models. Although previous studies and packages in the field of heterogeneous computing have focused on GPUs as accelerators, FPGAs are an extremely promising option as well. A series of workflows are developed to establish the performance capabilities of FPGAs as a service. Multiple different devices and a range of algorithms for use in high energy physics are studied. For a small, dense network, the throughput can be improved by an order of magnitude with respect to GPUs as a service. For large convolutional networks, the throughput is found to be comparable to GPUs as a service. This work represents the first open-source FPGAs-as-a-service toolkit. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.08556v1-abstract-full').style.display = 'none'; document.getElementById('2010.08556v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 7 figures, to appear in proceedings of the 2020 IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-CONF-20-426-SCD </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2020 IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC), 2020, pp. 38-47 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.04509">arXiv:2009.04509</a> <span> [<a href="https://arxiv.org/pdf/2009.04509">pdf</a>, <a href="https://arxiv.org/format/2009.04509">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3389/fdata.2020.604083">10.3389/fdata.2020.604083 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> GPU-accelerated machine learning inference as a service for computing in neutrino experiments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wang%2C+M">Michael Wang</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+T">Tingjun Yang</a>, <a href="/search/cs?searchtype=author&query=Flechas%2C+M+A">Maria Acosta Flechas</a>, <a href="/search/cs?searchtype=author&query=Harris%2C+P">Philip Harris</a>, <a href="/search/cs?searchtype=author&query=Hawks%2C+B">Benjamin Hawks</a>, <a href="/search/cs?searchtype=author&query=Holzman%2C+B">Burt Holzman</a>, <a href="/search/cs?searchtype=author&query=Knoepfel%2C+K">Kyle Knoepfel</a>, <a href="/search/cs?searchtype=author&query=Krupa%2C+J">Jeffrey Krupa</a>, <a href="/search/cs?searchtype=author&query=Pedro%2C+K">Kevin Pedro</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+N">Nhan Tran</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2009.04509v2-abstract-short" style="display: inline;"> Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volumes of such experiments are rapidly increasing. The demand to process billions of neutrino events with many machine learning algorithm inferences crea… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.04509v2-abstract-full').style.display = 'inline'; document.getElementById('2009.04509v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.04509v2-abstract-full" style="display: none;"> Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volumes of such experiments are rapidly increasing. The demand to process billions of neutrino events with many machine learning algorithm inferences creates a computing challenge. We explore a computing model in which heterogeneous computing with GPU coprocessors is made available as a web service. The coprocessors can be efficiently and elastically deployed to provide the right amount of computing for a given processing task. With our approach, Services for Optimized Network Inference on Coprocessors (SONIC), we integrate GPU acceleration specifically for the ProtoDUNE-SP reconstruction chain without disrupting the native computing workflow. With our integrated framework, we accelerate the most time-consuming task, track and particle shower hit identification, by a factor of 17. This results in a factor of 2.7 reduction in the total processing time when compared with CPU-only production. For this particular task, only 1 GPU is required for every 68 CPU threads, providing a cost-effective solution. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.04509v2-abstract-full').style.display = 'none'; document.getElementById('2009.04509v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages, 7 figures, 2 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-PUB-20-428-ND-SCD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.10359">arXiv:2007.10359</a> <span> [<a href="https://arxiv.org/pdf/2007.10359">pdf</a>, <a href="https://arxiv.org/format/2007.10359">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1088/2632-2153/abec21">10.1088/2632-2153/abec21 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> GPU coprocessors as a service for deep learning inference in high energy physics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Krupa%2C+J">Jeffrey Krupa</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+K">Kelvin Lin</a>, <a href="/search/cs?searchtype=author&query=Flechas%2C+M+A">Maria Acosta Flechas</a>, <a href="/search/cs?searchtype=author&query=Dinsmore%2C+J">Jack Dinsmore</a>, <a href="/search/cs?searchtype=author&query=Duarte%2C+J">Javier Duarte</a>, <a href="/search/cs?searchtype=author&query=Harris%2C+P">Philip Harris</a>, <a href="/search/cs?searchtype=author&query=Hauck%2C+S">Scott Hauck</a>, <a href="/search/cs?searchtype=author&query=Holzman%2C+B">Burt Holzman</a>, <a href="/search/cs?searchtype=author&query=Hsu%2C+S">Shih-Chieh Hsu</a>, <a href="/search/cs?searchtype=author&query=Klijnsma%2C+T">Thomas Klijnsma</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+M">Mia Liu</a>, <a href="/search/cs?searchtype=author&query=Pedro%2C+K">Kevin Pedro</a>, <a href="/search/cs?searchtype=author&query=Rankin%2C+D">Dylan Rankin</a>, <a href="/search/cs?searchtype=author&query=Suaysom%2C+N">Natchanon Suaysom</a>, <a href="/search/cs?searchtype=author&query=Trahms%2C+M">Matt Trahms</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+N">Nhan Tran</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2007.10359v2-abstract-short" style="display: inline;"> In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN Large Hadron Collider (LHC), these two issues will confront one another as the collider is upgraded for high luminosity running. Alternative processors such as graphics processing units (GPUs) can resolv… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.10359v2-abstract-full').style.display = 'inline'; document.getElementById('2007.10359v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.10359v2-abstract-full" style="display: none;"> In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN Large Hadron Collider (LHC), these two issues will confront one another as the collider is upgraded for high luminosity running. Alternative processors such as graphics processing units (GPUs) can resolve this confrontation provided that algorithms can be sufficiently accelerated. In many cases, algorithmic speedups are found to be largest through the adoption of deep learning algorithms. We present a comprehensive exploration of the use of GPU-based hardware acceleration for deep learning inference within the data reconstruction workflow of high energy physics. We present several realistic examples and discuss a strategy for the seamless integration of coprocessors so that the LHC can maintain, if not exceed, its current performance throughout its running. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.10359v2-abstract-full').style.display = 'none'; document.getElementById('2007.10359v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">26 pages, 7 figures, 2 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-PUB-20-338-E-SCD </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Mach. Learn.: Sci. Technol. 2 (2021) 035005 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.08988">arXiv:1904.08988</a> <span> [<a href="https://arxiv.org/pdf/1904.08988">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1051/epjconf/201921403060">10.1051/epjconf/201921403060 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> HEPCloud, an Elastic Hybrid HEP Facility using an Intelligent Decision Support System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mhashilkar%2C+P">Parag Mhashilkar</a>, <a href="/search/cs?searchtype=author&query=Altunay%2C+M">Mine Altunay</a>, <a href="/search/cs?searchtype=author&query=Berman%2C+E">Eileen Berman</a>, <a href="/search/cs?searchtype=author&query=Dagenhart%2C+D">David Dagenhart</a>, <a href="/search/cs?searchtype=author&query=Fuess%2C+S">Stuart Fuess</a>, <a href="/search/cs?searchtype=author&query=Holzman%2C+B">Burt Holzman</a>, <a href="/search/cs?searchtype=author&query=Kowalkowski%2C+J">James Kowalkowski</a>, <a href="/search/cs?searchtype=author&query=Litvintsev%2C+D">Dmitry Litvintsev</a>, <a href="/search/cs?searchtype=author&query=Lu%2C+Q">Qiming Lu</a>, <a href="/search/cs?searchtype=author&query=Moibenko%2C+A">Alexander Moibenko</a>, <a href="/search/cs?searchtype=author&query=Paterno%2C+M">Marc Paterno</a>, <a href="/search/cs?searchtype=author&query=Spentzouris%2C+P">Panagiotis Spentzouris</a>, <a href="/search/cs?searchtype=author&query=Timm%2C+S">Steven Timm</a>, <a href="/search/cs?searchtype=author&query=Tiradani%2C+A">Anthony Tiradani</a>, <a href="/search/cs?searchtype=author&query=Vaandering%2C+E">Eric Vaandering</a>, <a href="/search/cs?searchtype=author&query=Hover%2C+J">John Hover</a>, <a href="/search/cs?searchtype=author&query=Bejar%2C+J+C">Jose Caballero Bejar</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.08988v1-abstract-short" style="display: inline;"> HEPCloud is rapidly becoming the primary system for provisioning compute resources for all Fermilab-affiliated experiments. In order to reliably meet the peak demands of the next generation of High Energy Physics experiments, Fermilab must plan to elastically expand its computational capabilities to cover the forecasted need. Commercial cloud and allocation-based High Performance Computing (HPC) r… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.08988v1-abstract-full').style.display = 'inline'; document.getElementById('1904.08988v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.08988v1-abstract-full" style="display: none;"> HEPCloud is rapidly becoming the primary system for provisioning compute resources for all Fermilab-affiliated experiments. In order to reliably meet the peak demands of the next generation of High Energy Physics experiments, Fermilab must plan to elastically expand its computational capabilities to cover the forecasted need. Commercial cloud and allocation-based High Performance Computing (HPC) resources both have explicit and implicit costs that must be considered when deciding when to provision these resources, and at which scale. In order to support such provisioning in a manner consistent with organizational business rules and budget constraints, we have developed a modular intelligent decision support system (IDSS) to aid in the automatic provisioning of resources spanning multiple cloud providers, multiple HPC centers, and grid computing federations. In this paper, we discuss the goals and architecture of the HEPCloud Facility, the architecture of the IDSS, and our early experience in using the IDSS for automated facility expansion both at Fermi and Brookhaven National Laboratory. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.08988v1-abstract-full').style.display = 'none'; document.getElementById('1904.08988v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-CONF-18-658-CD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1806.03224">arXiv:1806.03224</a> <span> [<a href="https://arxiv.org/pdf/1806.03224">pdf</a>, <a href="https://arxiv.org/format/1806.03224">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Intelligently-automated facilities expansion with the HEPCloud Decision Engine </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Altunay%2C+M">Mine Altunay</a>, <a href="/search/cs?searchtype=author&query=Dagenhart%2C+W+D">W. David Dagenhart</a>, <a href="/search/cs?searchtype=author&query=Fuess%2C+S">Stuart Fuess</a>, <a href="/search/cs?searchtype=author&query=Holzman%2C+B">Burt Holzman</a>, <a href="/search/cs?searchtype=author&query=Kowalkowski%2C+J">Jim Kowalkowski</a>, <a href="/search/cs?searchtype=author&query=Litvintsev%2C+D">Dmitry Litvintsev</a>, <a href="/search/cs?searchtype=author&query=Lu%2C+Q">Qiming Lu</a>, <a href="/search/cs?searchtype=author&query=Mhashilkar%2C+P">Parag Mhashilkar</a>, <a href="/search/cs?searchtype=author&query=Moibenko%2C+A">Alexander Moibenko</a>, <a href="/search/cs?searchtype=author&query=Paterno%2C+M">Marc Paterno</a>, <a href="/search/cs?searchtype=author&query=Spentzouris%2C+P">Panagiotis Spentzouris</a>, <a href="/search/cs?searchtype=author&query=Timm%2C+S">Steven Timm</a>, <a href="/search/cs?searchtype=author&query=Tiradani%2C+A">Anthony Tiradani</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1806.03224v2-abstract-short" style="display: inline;"> The next generation of High Energy Physics experiments are expected to generate exabytes of data---two orders of magnitude greater than the current generation. In order to reliably meet peak demands, facilities must either plan to provision enough resources to cover the forecasted need, or find ways to elastically expand their computational capabilities. Commercial cloud and allocation-based High… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.03224v2-abstract-full').style.display = 'inline'; document.getElementById('1806.03224v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1806.03224v2-abstract-full" style="display: none;"> The next generation of High Energy Physics experiments are expected to generate exabytes of data---two orders of magnitude greater than the current generation. In order to reliably meet peak demands, facilities must either plan to provision enough resources to cover the forecasted need, or find ways to elastically expand their computational capabilities. Commercial cloud and allocation-based High Performance Computing (HPC) resources both have explicit and implicit costs that must be considered when deciding when to provision these resources, and to choose an appropriate scale. In order to support such provisioning in a manner consistent with organizational business rules and budget constraints, we have developed a modular intelligent decision support system (IDSS) to aid in the automatic provisioning of resources---spanning multiple cloud providers, multiple HPC centers, and grid computing federations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.03224v2-abstract-full').style.display = 'none'; document.getElementById('1806.03224v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 June, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 June, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2018)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FNAL CONF-18-051-CD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1710.00100">arXiv:1710.00100</a> <span> [<a href="https://arxiv.org/pdf/1710.00100">pdf</a>, <a href="https://arxiv.org/format/1710.00100">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/s41781-017-0001-9">10.1007/s41781-017-0001-9 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Holzman%2C+B">Burt Holzman</a>, <a href="/search/cs?searchtype=author&query=Bauerdick%2C+L+A+T">Lothar A. T. Bauerdick</a>, <a href="/search/cs?searchtype=author&query=Bockelman%2C+B">Brian Bockelman</a>, <a href="/search/cs?searchtype=author&query=Dykstra%2C+D">Dave Dykstra</a>, <a href="/search/cs?searchtype=author&query=Fisk%2C+I">Ian Fisk</a>, <a href="/search/cs?searchtype=author&query=Fuess%2C+S">Stuart Fuess</a>, <a href="/search/cs?searchtype=author&query=Garzoglio%2C+G">Gabriele Garzoglio</a>, <a href="/search/cs?searchtype=author&query=Girone%2C+M">Maria Girone</a>, <a href="/search/cs?searchtype=author&query=Gutsche%2C+O">Oliver Gutsche</a>, <a href="/search/cs?searchtype=author&query=Hufnagel%2C+D">Dirk Hufnagel</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H">Hyunwoo Kim</a>, <a href="/search/cs?searchtype=author&query=Kennedy%2C+R">Robert Kennedy</a>, <a href="/search/cs?searchtype=author&query=Magini%2C+N">Nicolo Magini</a>, <a href="/search/cs?searchtype=author&query=Mason%2C+D">David Mason</a>, <a href="/search/cs?searchtype=author&query=Spentzouris%2C+P">Panagiotis Spentzouris</a>, <a href="/search/cs?searchtype=author&query=Tiradani%2C+A">Anthony Tiradani</a>, <a href="/search/cs?searchtype=author&query=Timm%2C+S">Steve Timm</a>, <a href="/search/cs?searchtype=author&query=Vaandering%2C+E+W">Eric W. Vaandering</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1710.00100v1-abstract-short" style="display: inline;"> Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly de… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.00100v1-abstract-full').style.display = 'inline'; document.getElementById('1710.00100v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1710.00100v1-abstract-full" style="display: none;"> Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing nterest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized both local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. In addition, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.00100v1-abstract-full').style.display = 'none'; document.getElementById('1710.00100v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 September, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages, 9 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Comput Softw Big Sci (2017) 1:1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1404.6929">arXiv:1404.6929</a> <span> [<a href="https://arxiv.org/pdf/1404.6929">pdf</a>, <a href="https://arxiv.org/format/1404.6929">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Power-aware applications for scientific cluster and distributed computing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abdurachmanov%2C+D">David Abdurachmanov</a>, <a href="/search/cs?searchtype=author&query=Elmer%2C+P">Peter Elmer</a>, <a href="/search/cs?searchtype=author&query=Eulisse%2C+G">Giulio Eulisse</a>, <a href="/search/cs?searchtype=author&query=Grosso%2C+P">Paola Grosso</a>, <a href="/search/cs?searchtype=author&query=Hillegas%2C+C">Curtis Hillegas</a>, <a href="/search/cs?searchtype=author&query=Holzman%2C+B">Burt Holzman</a>, <a href="/search/cs?searchtype=author&query=Janssen%2C+R+L">Ruben L. Janssen</a>, <a href="/search/cs?searchtype=author&query=Klous%2C+S">Sander Klous</a>, <a href="/search/cs?searchtype=author&query=Knight%2C+R">Robert Knight</a>, <a href="/search/cs?searchtype=author&query=Muzaffar%2C+S">Shahzad Muzaffar</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1404.6929v2-abstract-short" style="display: inline;"> The aggregate power use of computing hardware is an important cost factor in scientific cluster and distributed computing systems. The Worldwide LHC Computing Grid (WLCG) is a major example of such a distributed computing system, used primarily for high throughput computing (HTC) applications. It has a computing capacity and power consumption rivaling that of the largest supercomputers. The comput… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1404.6929v2-abstract-full').style.display = 'inline'; document.getElementById('1404.6929v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1404.6929v2-abstract-full" style="display: none;"> The aggregate power use of computing hardware is an important cost factor in scientific cluster and distributed computing systems. The Worldwide LHC Computing Grid (WLCG) is a major example of such a distributed computing system, used primarily for high throughput computing (HTC) applications. It has a computing capacity and power consumption rivaling that of the largest supercomputers. The computing capacity required from this system is also expected to grow over the next decade. Optimizing the power utilization and cost of such systems is thus of great interest. A number of trends currently underway will provide new opportunities for power-aware optimizations. We discuss how power-aware software applications and scheduling might be used to reduce power consumption, both as autonomous entities and as part of a (globally) distributed system. As concrete examples of computing centers we provide information on the large HEP-focused Tier-1 at FNAL, and the Tigress High Performance Computing Center at Princeton University, which provides HPC resources in a university context. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1404.6929v2-abstract-full').style.display = 'none'; document.getElementById('1404.6929v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 October, 2014; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 April, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2014. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to proceedings of International Symposium on Grids and Clouds (ISGC) 2014, 23-28 March 2014, Academia Sinica, Taipei, Taiwan</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/cs/0604109">arXiv:cs/0604109</a> <span> [<a href="https://arxiv.org/pdf/cs/0604109">pdf</a>, <a href="https://arxiv.org/ps/cs/0604109">ps</a>, <a href="https://arxiv.org/format/cs/0604109">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> CMS Software Distribution on the LCG and OSG Grids </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rabbertz%2C+K">K. Rabbertz</a>, <a href="/search/cs?searchtype=author&query=Thomas%2C+M">M. Thomas</a>, <a href="/search/cs?searchtype=author&query=Ashby%2C+S">S. Ashby</a>, <a href="/search/cs?searchtype=author&query=Corvo%2C+M">M. Corvo</a>, <a href="/search/cs?searchtype=author&query=Argir%C3%B2%2C+S">S. Argir貌</a>, <a href="/search/cs?searchtype=author&query=Darmenov%2C+N">N. Darmenov</a>, <a href="/search/cs?searchtype=author&query=Darwish%2C+R">R. Darwish</a>, <a href="/search/cs?searchtype=author&query=Evans%2C+D">D. Evans</a>, <a href="/search/cs?searchtype=author&query=Holzman%2C+B">B. Holzman</a>, <a href="/search/cs?searchtype=author&query=Ratnikova%2C+N">N. Ratnikova</a>, <a href="/search/cs?searchtype=author&query=Muzaffar%2C+S">S. Muzaffar</a>, <a href="/search/cs?searchtype=author&query=Nowack%2C+A">A. Nowack</a>, <a href="/search/cs?searchtype=author&query=Wildish%2C+T">T. Wildish</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+B">B. Kim</a>, <a href="/search/cs?searchtype=author&query=Weng%2C+J">J. Weng</a>, <a href="/search/cs?searchtype=author&query=B%C3%BCge%2C+V">V. B眉ge</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="cs/0604109v1-abstract-short" style="display: inline;"> The efficient exploitation of worldwide distributed storage and computing resources available in the grids require a robust, transparent and fast deployment of experiment specific software. The approach followed by the CMS experiment at CERN in order to enable Monte-Carlo simulations, data analysis and software development in an international collaboration is presented. The current status and fu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cs/0604109v1-abstract-full').style.display = 'inline'; document.getElementById('cs/0604109v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="cs/0604109v1-abstract-full" style="display: none;"> The efficient exploitation of worldwide distributed storage and computing resources available in the grids require a robust, transparent and fast deployment of experiment specific software. The approach followed by the CMS experiment at CERN in order to enable Monte-Carlo simulations, data analysis and software development in an international collaboration is presented. The current status and future improvement plans are described. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cs/0604109v1-abstract-full').style.display = 'none'; document.getElementById('cs/0604109v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 April, 2006; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2006. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">4 pages, 1 figure, latex with hyperrefs</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" 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