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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/2308.05326">arXiv:2308.05326</a> <span> [<a href="https://arxiv.org/pdf/2308.05326">pdf</a>, <a href="https://arxiv.org/format/2308.05326">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> OpenProteinSet: Training data for structural biology at scale </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahdritz%2C+G">Gustaf Ahdritz</a>, <a href="/search/cs?searchtype=author&query=Bouatta%2C+N">Nazim Bouatta</a>, <a href="/search/cs?searchtype=author&query=Kadyan%2C+S">Sachin Kadyan</a>, <a href="/search/cs?searchtype=author&query=Jarosch%2C+L">Lukas Jarosch</a>, <a href="/search/cs?searchtype=author&query=Berenberg%2C+D">Daniel Berenberg</a>, <a href="/search/cs?searchtype=author&query=Fisk%2C+I">Ian Fisk</a>, <a href="/search/cs?searchtype=author&query=Watkins%2C+A+M">Andrew M. Watkins</a>, <a href="/search/cs?searchtype=author&query=Ra%2C+S">Stephen Ra</a>, <a href="/search/cs?searchtype=author&query=Bonneau%2C+R">Richard Bonneau</a>, <a href="/search/cs?searchtype=author&query=AlQuraishi%2C+M">Mohammed AlQuraishi</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="2308.05326v1-abstract-short" style="display: inline;"> Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein structure prediction for decades. Recent breakthroughs like AlphaFold2 that use transformers to attend directly over large quantities of raw MSAs have reaffirmed their importance. Generation of MSAs is highly computationally… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.05326v1-abstract-full').style.display = 'inline'; document.getElementById('2308.05326v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.05326v1-abstract-full" style="display: none;"> Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein structure prediction for decades. Recent breakthroughs like AlphaFold2 that use transformers to attend directly over large quantities of raw MSAs have reaffirmed their importance. Generation of MSAs is highly computationally intensive, however, and no datasets comparable to those used to train AlphaFold2 have been made available to the research community, hindering progress in machine learning for proteins. To remedy this problem, we introduce OpenProteinSet, an open-source corpus of more than 16 million MSAs, associated structural homologs from the Protein Data Bank, and AlphaFold2 protein structure predictions. We have previously demonstrated the utility of OpenProteinSet by successfully retraining AlphaFold2 on it. We expect OpenProteinSet to be broadly useful as training and validation data for 1) diverse tasks focused on protein structure, function, and design and 2) large-scale multimodal machine learning research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.05326v1-abstract-full').style.display = 'none'; document.getElementById('2308.05326v1-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> 10 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.08868">arXiv:2209.08868</a> <span> [<a href="https://arxiv.org/pdf/2209.08868">pdf</a>, <a href="https://arxiv.org/format/2209.08868">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="High Energy Physics - Lattice">hep-lat</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Theory">hep-th</span> </div> </div> <p class="title is-5 mathjax"> Snowmass 2021 Computational Frontier CompF4 Topical Group Report: Storage and Processing Resource Access </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhimji%2C+W">W. Bhimji</a>, <a href="/search/cs?searchtype=author&query=Carder%2C+D">D. Carder</a>, <a href="/search/cs?searchtype=author&query=Dart%2C+E">E. Dart</a>, <a href="/search/cs?searchtype=author&query=Duarte%2C+J">J. Duarte</a>, <a href="/search/cs?searchtype=author&query=Fisk%2C+I">I. Fisk</a>, <a href="/search/cs?searchtype=author&query=Gardner%2C+R">R. Gardner</a>, <a href="/search/cs?searchtype=author&query=Guok%2C+C">C. Guok</a>, <a href="/search/cs?searchtype=author&query=Jayatilaka%2C+B">B. Jayatilaka</a>, <a href="/search/cs?searchtype=author&query=Lehman%2C+T">T. Lehman</a>, <a href="/search/cs?searchtype=author&query=Lin%2C+M">M. Lin</a>, <a href="/search/cs?searchtype=author&query=Maltzahn%2C+C">C. Maltzahn</a>, <a href="/search/cs?searchtype=author&query=McKee%2C+S">S. McKee</a>, <a href="/search/cs?searchtype=author&query=Neubauer%2C+M+S">M. S. Neubauer</a>, <a href="/search/cs?searchtype=author&query=Rind%2C+O">O. Rind</a>, <a href="/search/cs?searchtype=author&query=Shadura%2C+O">O. Shadura</a>, <a href="/search/cs?searchtype=author&query=Tran%2C+N+V">N. V. Tran</a>, <a href="/search/cs?searchtype=author&query=van+Gemmeren%2C+P">P. van Gemmeren</a>, <a href="/search/cs?searchtype=author&query=Watts%2C+G">G. Watts</a>, <a href="/search/cs?searchtype=author&query=Weaver%2C+B+A">B. A. Weaver</a>, <a href="/search/cs?searchtype=author&query=W%C3%BCrthwein%2C+F">F. W眉rthwein</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="2209.08868v2-abstract-short" style="display: inline;"> Computing plays a significant role in all areas of high energy physics. The Snowmass 2021 CompF4 topical group's scope is facilities R&D, where we consider "facilities" as the computing hardware and software infrastructure inside the data centers plus the networking between data centers, irrespective of who owns them, and what policies are applied for using them. In other words, it includes commer… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.08868v2-abstract-full').style.display = 'inline'; document.getElementById('2209.08868v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.08868v2-abstract-full" style="display: none;"> Computing plays a significant role in all areas of high energy physics. The Snowmass 2021 CompF4 topical group's scope is facilities R&D, where we consider "facilities" as the computing hardware and software infrastructure inside the data centers plus the networking between data centers, irrespective of who owns them, and what policies are applied for using them. In other words, it includes commercial clouds, federally funded High Performance Computing (HPC) systems for all of science, and systems funded explicitly for a given experimental or theoretical program. This topical group report summarizes the findings and recommendations for the storage, processing, networking and associated software service infrastructures for future high energy physics research, based on the discussions organized through the Snowmass 2021 community study. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.08868v2-abstract-full').style.display = 'none'; document.getElementById('2209.08868v2-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, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">Snowmass 2021 Computational Frontier CompF4 topical group report. v2: Expanded introduction. Updated author list. 52 pages, 6 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/1901.07143">arXiv:1901.07143</a> <span> [<a href="https://arxiv.org/pdf/1901.07143">pdf</a>, <a href="https://arxiv.org/format/1901.07143">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 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/201921406030">10.1051/epjconf/201921406030 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Using Big Data Technologies for HEP Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cremonesi%2C+M">Matteo Cremonesi</a>, <a href="/search/cs?searchtype=author&query=Bellini%2C+C">Claudio Bellini</a>, <a href="/search/cs?searchtype=author&query=Bian%2C+B">Bianny Bian</a>, <a href="/search/cs?searchtype=author&query=Canali%2C+L">Luca Canali</a>, <a href="/search/cs?searchtype=author&query=Dimakopoulos%2C+V">Vasileios Dimakopoulos</a>, <a href="/search/cs?searchtype=author&query=Elmer%2C+P">Peter Elmer</a>, <a href="/search/cs?searchtype=author&query=Fisk%2C+I">Ian Fisk</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=Hoh%2C+S">Siew-Yan Hoh</a>, <a href="/search/cs?searchtype=author&query=Jayatilaka%2C+B">Bo Jayatilaka</a>, <a href="/search/cs?searchtype=author&query=Khristenko%2C+V">Viktor Khristenko</a>, <a href="/search/cs?searchtype=author&query=Luiselli%2C+A">Andrea Luiselli</a>, <a href="/search/cs?searchtype=author&query=Melo%2C+A">Andrew Melo</a>, <a href="/search/cs?searchtype=author&query=Evangelos%2C+E">Evangelos Evangelos</a>, <a href="/search/cs?searchtype=author&query=Olivito%2C+D">Dominick Olivito</a>, <a href="/search/cs?searchtype=author&query=Pazzini%2C+J">Jacopo Pazzini</a>, <a href="/search/cs?searchtype=author&query=Pivarski%2C+J">Jim Pivarski</a>, <a href="/search/cs?searchtype=author&query=Svyatkovskiy%2C+A">Alexey Svyatkovskiy</a>, <a href="/search/cs?searchtype=author&query=Zanetti%2C+M">Marco Zanetti</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="1901.07143v1-abstract-short" style="display: inline;"> The HEP community is approaching an era were the excellent performances of the particle accelerators in delivering collision at high rate will force the experiments to record a large amount of information. The growing size of the datasets could potentially become a limiting factor in the capability to produce scientific results timely and efficiently. Recently, new technologies and new approaches… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.07143v1-abstract-full').style.display = 'inline'; document.getElementById('1901.07143v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1901.07143v1-abstract-full" style="display: none;"> The HEP community is approaching an era were the excellent performances of the particle accelerators in delivering collision at high rate will force the experiments to record a large amount of information. The growing size of the datasets could potentially become a limiting factor in the capability to produce scientific results timely and efficiently. Recently, new technologies and new approaches have been developed in industry to answer to the necessity to retrieve information as quickly as possible to analyze PB and EB datasets. Providing the scientists with these modern computing tools will lead to rethinking the principles of data analysis in HEP, making the overall scientific process faster and smoother. In this paper, we are presenting the latest developments and the most recent results on the usage of Apache Spark for HEP analysis. The study aims at evaluating the efficiency of the application of the new tools both quantitatively, by measuring the performances, and qualitatively, focusing on the user experience. The first goal is achieved by developing a data reduction facility: working together with CERN Openlab and Intel, CMS replicates a real physics search using Spark-based technologies, with the ambition of reducing 1 PB of public data in 5 hours, collected by the CMS experiment, to 1 TB of data in a format suitable for physics analysis. The second goal is achieved by implementing multiple physics use-cases in Apache Spark using as input preprocessed datasets derived from official CMS data and simulation. By performing different end-analyses up to the publication plots on different hardware, feasibility, usability and portability are compared to the ones of a traditional ROOT-based workflow. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.07143v1-abstract-full').style.display = 'none'; document.getElementById('1901.07143v1-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> 21 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1711.00375">arXiv:1711.00375</a> <span> [<a href="https://arxiv.org/pdf/1711.00375">pdf</a>, <a href="https://arxiv.org/format/1711.00375">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 Analysis and Data Reduction with Apache Spark </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gutsche%2C+O">Oliver Gutsche</a>, <a href="/search/cs?searchtype=author&query=Canali%2C+L">Luca Canali</a>, <a href="/search/cs?searchtype=author&query=Cremer%2C+I">Illia Cremer</a>, <a href="/search/cs?searchtype=author&query=Cremonesi%2C+M">Matteo Cremonesi</a>, <a href="/search/cs?searchtype=author&query=Elmer%2C+P">Peter Elmer</a>, <a href="/search/cs?searchtype=author&query=Fisk%2C+I">Ian Fisk</a>, <a href="/search/cs?searchtype=author&query=Girone%2C+M">Maria Girone</a>, <a href="/search/cs?searchtype=author&query=Jayatilaka%2C+B">Bo Jayatilaka</a>, <a href="/search/cs?searchtype=author&query=Kowalkowski%2C+J">Jim Kowalkowski</a>, <a href="/search/cs?searchtype=author&query=Khristenko%2C+V">Viktor Khristenko</a>, <a href="/search/cs?searchtype=author&query=Motesnitsalis%2C+E">Evangelos Motesnitsalis</a>, <a href="/search/cs?searchtype=author&query=Pivarski%2C+J">Jim Pivarski</a>, <a href="/search/cs?searchtype=author&query=Sehrish%2C+S">Saba Sehrish</a>, <a href="/search/cs?searchtype=author&query=Surdy%2C+K">Kacper Surdy</a>, <a href="/search/cs?searchtype=author&query=Svyatkovskiy%2C+A">Alexey Svyatkovskiy</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="1711.00375v1-abstract-short" style="display: inline;"> Experimental Particle Physics has been at the forefront of analyzing the world's largest datasets for decades. The HEP community was among the first to develop suitable software and computing tools for this task. In recent times, new toolkits and systems for distributed data processing, collectively called "Big Data" technologies have emerged from industry and open source projects to support the a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.00375v1-abstract-full').style.display = 'inline'; document.getElementById('1711.00375v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1711.00375v1-abstract-full" style="display: none;"> Experimental Particle Physics has been at the forefront of analyzing the world's largest datasets for decades. The HEP community was among the first to develop suitable software and computing tools for this task. In recent times, new toolkits and systems for distributed data processing, collectively called "Big Data" technologies have emerged from industry and open source projects to support the analysis of Petabyte and Exabyte datasets in industry. While the principles of data analysis in HEP have not changed (filtering and transforming experiment-specific data formats), these new technologies use different approaches and tools, promising a fresh look at analysis of very large datasets that could potentially reduce the time-to-physics with increased interactivity. Moreover these new tools are typically actively developed by large communities, often profiting of industry resources, and under open source licensing. These factors result in a boost for adoption and maturity of the tools and for the communities supporting them, at the same time helping in reducing the cost of ownership for the end-users. In this talk, we are presenting studies of using Apache Spark for end user data analysis. We are studying the HEP analysis workflow separated into two thrusts: the reduction of centrally produced experiment datasets and the end-analysis up to the publication plot. Studying the first thrust, CMS is working together with CERN openlab and Intel on the CMS Big Data Reduction Facility. The goal is to reduce 1 PB of official CMS data to 1 TB of ntuple output for analysis. We are presenting the progress of this 2-year project with first results of scaling up Spark-based HEP analysis. Studying the second thrust, we are presenting studies on using Apache Spark for a CMS Dark Matter physics search, comparing Spark's feasibility, usability and performance to the ROOT-based analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.00375v1-abstract-full').style.display = 'none'; document.getElementById('1711.00375v1-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> 31 October, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">Proceedings for 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017). arXiv admin note: text overlap with arXiv:1703.04171</span> </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/1012.1980">arXiv:1012.1980</a> <span> [<a href="https://arxiv.org/pdf/1012.1980">pdf</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="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"> First Experiences with LHC Grid Computing and Distributed Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fisk%2C+I">Ian Fisk</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="1012.1980v1-abstract-short" style="display: inline;"> In this presentation the experiences of the LHC experiments using grid computing were presented with a focus on experience with distributed analysis. After many years of development, preparation, exercises, and validation the LHC (Large Hadron Collider) experiments are in operations. The computing infrastructure has been heavily utilized in the first 6 months of data collection. The general experi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1012.1980v1-abstract-full').style.display = 'inline'; document.getElementById('1012.1980v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1012.1980v1-abstract-full" style="display: none;"> In this presentation the experiences of the LHC experiments using grid computing were presented with a focus on experience with distributed analysis. After many years of development, preparation, exercises, and validation the LHC (Large Hadron Collider) experiments are in operations. The computing infrastructure has been heavily utilized in the first 6 months of data collection. The general experience of exploiting the grid infrastructure for organized processing and preparation is described, as well as the successes employing the infrastructure for distributed analysis. At the end the expected evolution and future plans are outlined. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1012.1980v1-abstract-full').style.display = 'none'; document.getElementById('1012.1980v1-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> 9 December, 2010; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2010. </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">6 pages, 2 figures, Proceedings of the Hadron Collider Physics Symposium 2010 (HCP2010), Toronto Canada</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/0305066">arXiv:cs/0305066</a> <span> [<a href="https://arxiv.org/pdf/cs/0305066">pdf</a>, <a href="https://arxiv.org/ps/cs/0305066">ps</a>, <a href="https://arxiv.org/format/cs/0305066">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"> The CMS Integration Grid Testbed </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Graham%2C+G+E">Gregory E. Graham</a>, <a href="/search/cs?searchtype=author&query=Afaq%2C+M+A">M. Anzar Afaq</a>, <a href="/search/cs?searchtype=author&query=Aziz%2C+S">Shafqat Aziz</a>, <a href="/search/cs?searchtype=author&query=Bauerdick%2C+L+A+T">L. A. T. Bauerdick</a>, <a href="/search/cs?searchtype=author&query=Ernst%2C+M">Michael Ernst</a>, <a href="/search/cs?searchtype=author&query=Kaiser%2C+J">Joseph Kaiser</a>, <a href="/search/cs?searchtype=author&query=Ratnikova%2C+N">Natalia Ratnikova</a>, <a href="/search/cs?searchtype=author&query=Wenzel%2C+H">Hans Wenzel</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Y">Yujun Wu</a>, <a href="/search/cs?searchtype=author&query=Aslakson%2C+E">Erik Aslakson</a>, <a href="/search/cs?searchtype=author&query=Bunn%2C+J">Julian Bunn</a>, <a href="/search/cs?searchtype=author&query=Iqbal%2C+S">Saima Iqbal</a>, <a href="/search/cs?searchtype=author&query=Legrand%2C+I">Iosif Legrand</a>, <a href="/search/cs?searchtype=author&query=Newman%2C+H">Harvey Newman</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+S">Suresh Singh</a>, <a href="/search/cs?searchtype=author&query=Steenberg%2C+C">Conrad Steenberg</a>, <a href="/search/cs?searchtype=author&query=Branson%2C+J">James Branson</a>, <a href="/search/cs?searchtype=author&query=Fisk%2C+I">Ian Fisk</a>, <a href="/search/cs?searchtype=author&query=Letts%2C+J">James Letts</a>, <a href="/search/cs?searchtype=author&query=Arbree%2C+A">Adam Arbree</a>, <a href="/search/cs?searchtype=author&query=Avery%2C+P">Paul Avery</a>, <a href="/search/cs?searchtype=author&query=Bourilkov%2C+D">Dimitri Bourilkov</a>, <a href="/search/cs?searchtype=author&query=Cavanaugh%2C+R">Richard Cavanaugh</a>, <a href="/search/cs?searchtype=author&query=Rodriguez%2C+J">Jorge Rodriguez</a>, <a href="/search/cs?searchtype=author&query=Kategari%2C+S">Suchindra Kategari</a> , et al. (5 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="cs/0305066v2-abstract-short" style="display: inline;"> The CMS Integration Grid Testbed (IGT) comprises USCMS Tier-1 and Tier-2 hardware at the following sites: the California Institute of Technology, Fermi National Accelerator Laboratory, the University of California at San Diego, and the University of Florida at Gainesville. The IGT runs jobs using the Globus Toolkit with a DAGMan and Condor-G front end. The virtual organization (VO) is managed us… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cs/0305066v2-abstract-full').style.display = 'inline'; document.getElementById('cs/0305066v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="cs/0305066v2-abstract-full" style="display: none;"> The CMS Integration Grid Testbed (IGT) comprises USCMS Tier-1 and Tier-2 hardware at the following sites: the California Institute of Technology, Fermi National Accelerator Laboratory, the University of California at San Diego, and the University of Florida at Gainesville. The IGT runs jobs using the Globus Toolkit with a DAGMan and Condor-G front end. The virtual organization (VO) is managed using VO management scripts from the European Data Grid (EDG). Gridwide monitoring is accomplished using local tools such as Ganglia interfaced into the Globus Metadata Directory Service (MDS) and the agent based Mona Lisa. Domain specific software is packaged and installed using the Distrib ution After Release (DAR) tool of CMS, while middleware under the auspices of the Virtual Data Toolkit (VDT) is distributed using Pacman. During a continuo us two month span in Fall of 2002, over 1 million official CMS GEANT based Monte Carlo events were generated and returned to CERN for analysis while being demonstrated at SC2002. In this paper, we describe the process that led to one of the world's first continuously available, functioning grids. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cs/0305066v2-abstract-full').style.display = 'none'; document.getElementById('cs/0305066v2-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> 10 June, 2003; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 May, 2003; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2003. </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">CHEP 2003 MOCT010</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> A.0; C.2.4 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> eConfC0303241:MOCT010B,2003 </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" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 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