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The simulation of the calorimeter response is particularly demanding, consuming about 80% of the total simulation time. In order to make the best use of the available computational resources, fast simulation tools based on Machine Learning techniques have been developed to simulate the calorimeter response faster than Geant4 while maintaining a high level of accuracy. One such tool, developed by the ATLAS Collaboration and currently in production for LHC Run 3, is FastCaloGAN, which uses Generative Adversarial Networks (GANs) to generate electromagnetic and hadronic showers. To facilitate the training and optimisation of the GANs, and to enable a more efficient use of computational resources, a container-based system, FastCaloGANtainer, facilitates the deployment of the FastCaloGAN training on complementary high-performance resources such as High Performance Computing (HPC) farms and ensures its operational independence from the underlying system. This poster presents the latest developments in FastCaloGAN and FastCaloGANtainer, discussing their technical details and recent improvements in terms of Physics and computational performance. For FastCaloGAN, these improvements include an improved voxelisation and extension to further use cases (e.g. particle types not yet covered), while for FastCaloGANtainer they concern its deployment on a wider variety of resources with multi-CPU/GPU nodes and different architectures (including cutting-edge HPC clusters such as Leonardo at CINECA in Bologna, Italy). Corchia, Federico Andrea; Rinaldi, Lorenzo; Franchini, Matteo" /> <meta name="keywords" content="CERN Document Server, WebSearch, ATLAS Preprints" /> <script type="text/javascript" src="https://cds.cern.ch/js/jquery.min.js"></script>  <link rel="stylesheet" href="https://cds.cern.ch/img/webnews.css" type="text/css" />  <script type="text/javascript" src="https://cds.cern.ch/js/webnews.js?v=20131009"></script> <meta property="fb:app_id" content="137353533001720"/> <script type="text/x-mathjax-config"> MathJax.Hub.Config({ tex2jax: {inlineMath: [['$','$']], processEscapes: true}, showProcessingMessages: false, messageStyle: "none" }); </script> <script src="/MathJax/MathJax.js?config=TeX-AMS_CHTML" type="text/javascript"> </script>  <meta content="Recent Advances in the GAN-Based Fast Calorimeter Simulation of the ATLAS Experiment" name="citation_title" /> <meta content="Corchia, Federico Andrea" name="citation_author" /> <meta content="Rinaldi, Lorenzo" name="citation_author" /> <meta content="Franchini, Matteo" name="citation_author" /> <meta content="2024/11/27" name="citation_publication_date" /> <meta name="citation_online_date" content="2024/11/27"> <meta content="ATL-SOFT-SLIDE-2024-600" name="citation_technical_report_number" /> <meta content="ATL-COM-SOFT-2024-105" name="citation_technical_report_institution" /> <meta name="citation_pdf_url" content="https://cds.cern.ch/record/2918115/files/ATL-SOFT-SLIDE-2024-600.pdf" />  <meta content="Recent Advances in the GAN-Based Fast Calorimeter Simulation of the ATLAS Experiment" property="og:title" /> <meta content="website" property="og:type" /> <meta content="website" property="og:type" /> <meta content="https://cds.cern.ch/record/2918115" property="og:url" /> <meta content="CERN Document Server" property="og:site_name" /> <meta content="Simulation of the detector response is a major computational challenge in modern High-Energy Physics experiments, accounting for about 40% of the total computational resources used in ATLAS. The simulation of the calorimeter response is particularly demanding, consuming about 80% of the total simulation time. In order to make the best use of the available computational resources, fast simulation tools based on Machine Learning techniques have been developed to simulate the calorimeter response faster than Geant4 while maintaining a high level of accuracy. One such tool, developed by the ATLAS Collaboration and currently in production for LHC Run 3, is FastCaloGAN, which uses Generative Adversarial Networks (GANs) to generate electromagnetic and hadronic showers. To facilitate the training and optimisation of the GANs, and to enable a more efficient use of computational resources, a container-based system, FastCaloGANtainer, facilitates the deployment of the FastCaloGAN training on complementary high-performance resources such as High Performance Computing (HPC) farms and ensures its operational independence from the underlying system. This poster presents the latest developments in FastCaloGAN and FastCaloGANtainer, discussing their technical details and recent improvements in terms of Physics and computational performance. For FastCaloGAN, these improvements include an improved voxelisation and extension to further use cases (e.g. particle types not yet covered), while for FastCaloGANtainer they concern its deployment on a wider variety of resources with multi-CPU/GPU nodes and different architectures (including cutting-edge HPC clusters such as Leonardo at CINECA in Bologna, Italy)." property="og:description" />  <meta content="summary" name="twitter:card" /> <meta content="summary" name="twitter:card" /> <style></style> </head> <body class="ATLAS32Preprints search" lang="en">  <div id="cern-toolbar"> <h1><a href="http://cern.ch" title="CERN">CERN <span>Accelerating science</span></a></h1> <ul> <li class="cern-accountlinks"><a class="cern-account" href="https://cds.cern.ch/youraccount/login?ln=en&referer=https%3A//cds.cern.ch/record/2918115/%3Fln%3Den" title="Sign in to your CERN account">Sign in</a></li> <li><a class="cern-directory" href="http://cern.ch/directory" title="Search CERN resources and browse the directory">Directory</a></li> </ul> </div>   <div role="banner" class="clearfix" id="header"> <div class="header-inner inner"> <hgroup class="clearfix"> <h2 id="site-name"> <a rel="home" title="Home" href="/"><span>CERN Document Server</span></a> </h2> <h3 id="site-slogan">Access articles, reports and multimedia content in HEP</h3> </hgroup> <div role="navigation" id="main-navigation" class="cdsmenu"> <h2 class="element-invisible">Main menu</h2><ul class="links inline clearfix"> <li class="menu-386 first active-trail"><a class="active-trail" href="https://cds.cern.ch/?ln=en">Search</a></li> <li class="menu-444 "><a class="" title="" href="https://cds.cern.ch/submit?ln=en">Submit</a></li> <li class="menu-426 "><a class="" href="https://cds.cern.ch/help/?ln=en">Help</a></li> <li class="leaf hassubcdsmenu"> <a hreflang="en" class="header" href="https://cds.cern.ch/youraccount/display?ln=en">Personalize</a> <ul class="subsubcdsmenu"><li><a href="https://cds.cern.ch/youralerts/list?ln=en">Your alerts</a></li><li><a href="https://cds.cern.ch/yourbaskets/display?ln=en">Your baskets</a></li><li><a href="https://cds.cern.ch/yourcomments?ln=en">Your comments</a></li><li><a href="https://cds.cern.ch/youralerts/display?ln=en">Your searches</a></li></ul></li> </ul> </div> </div> </div>  <table class="navtrailbox"> <tr> <td class="navtrailboxbody"> <a href="/?ln=en" class="navtrail">Home</a> > <a href="/collection/CERN%20Experiments?ln=en" class="navtrail">CERN Experiments</a> > <a href="/collection/LHC%20Experiments?ln=en" class="navtrail">LHC Experiments</a> > <a href="/collection/ATLAS?ln=en" class="navtrail">ATLAS</a> > <a href="/collection/ATLAS%20Preprints?ln=en" class="navtrail">ATLAS Preprints</a> > Recent Advances in the GAN-Based Fast Calorimeter Simulation of the ATLAS Experiment </td> </tr> </table> </div> <div class="pagebody"><div class="pagebodystripemiddle"> <div class="detailedrecordbox"> <div class="detailedrecordtabs"> <div> <ul class="detailedrecordtabs"><li class="on first"><a href="/record/2918115/?ln=en">Information </a></li><li class=""><a href="/record/2918115/comments?ln=en">Discussion (0) </a></li><li class=""><a href="/record/2918115/files?ln=en">Files </a></li></ul> <div id="tabsSpacer" style="clear:both;height:0px"> </div></div> </div> <div class="detailedrecordboxcontent"> <div class="top-left-folded"></div> <div class="top-right-folded"></div> <div class="inside">  <abbr class="unapi-id" title="2918115"></abbr> <style type="text/css">  </style> <table class="formatRecordTableFullWidth" > <tr> <td class="formatRecordHeader" style="background-image: url('https://cds.cern.ch/img/journals.jpg');" colspan="2">  ATLAS Slides </td> </tr> <tr><td class="formatRecordLabel"> Report number </td><td style="padding-left:5px;">ATL-SOFT-SLIDE-2024-600</td></tr> <tr><td class="formatRecordLabel"> Title </td><td style="padding-left:5px;"><b>Recent Advances in the GAN-Based Fast Calorimeter Simulation of the ATLAS Experiment</b></td></tr>  <tr><td class="formatRecordLabel"> <span style="white-space:nowrap;">Author(s)</span> </td><td style="padding-left:5px;"><a href="https://cds.cern.ch/search?f=author&p=Corchia%2C%20Federico%20Andrea&ln=en">Corchia, Federico Andrea</a> (Universita e INFN, Bologna (IT)) ; <a href="https://cds.cern.ch/search?f=author&p=Rinaldi%2C%20Lorenzo&ln=en">Rinaldi, Lorenzo</a> (Universita e INFN, Bologna (IT)) ; <a href="https://cds.cern.ch/search?f=author&p=Franchini%2C%20Matteo&ln=en">Franchini, Matteo</a> (Universita e INFN, Bologna (IT))</td></tr> <tr><td class="formatRecordLabel"> Corporate <span style="white-space:nowrap;">author(s)</span> </td><td style="padding-left:5px;">The ATLAS collaboration</td></tr> <tr><td class="formatRecordLabel"> Collaboration </td><td style="padding-left:5px;">ATLAS Collaboration</td></tr> <tr><td class="formatRecordLabel"> Submitted by </td><td style="padding-left:5px;"><a href="mailto:corchia@bo.infn.it">corchia@bo.infn.it</a> on 27 Nov 2024</td></tr> <tr><td class="formatRecordLabel"> Subject category </td><td style="padding-left:5px;">Particle Physics - Experiment</td></tr> <tr><td class="formatRecordLabel"> Accelerator/Facility, Experiment </td><td style="padding-left:5px;"><a href="https://cds.cern.ch/search?p=CERN%20LHC&f=693__a">CERN LHC</a> ; <a href="https://cds.cern.ch/search?p=ATLAS&f=693__e">ATLAS</a></td></tr> <tr><td class="formatRecordLabel"> Abstract </td><td style="padding-left:5px;">Simulation of the detector response is a major computational challenge in modern High-Energy Physics experiments, accounting for about 40% of the total computational resources used in ATLAS. The simulation of the calorimeter response is particularly demanding, consuming about 80% of the total simulation time. In order to make the best use of the available computational resources, fast simulation tools based on Machine Learning techniques have been developed to simulate the calorimeter response faster than Geant4 while maintaining a high level of accuracy. One such tool, developed by the ATLAS Collaboration and currently in production for LHC Run 3, is FastCaloGAN, which uses Generative Adversarial Networks (GANs) to generate electromagnetic and hadronic showers. To facilitate the training and optimisation of the GANs, and to enable a more efficient use of computational resources, a container-based system, FastCaloGANtainer, facilitates the deployment of the FastCaloGAN training on complementary high-performance resources such as High Performance Computing (HPC) farms and ensures its operational independence from the underlying system. This poster presents the latest developments in FastCaloGAN and FastCaloGANtainer, discussing their technical details and recent improvements in terms of Physics and computational performance. For FastCaloGAN, these improvements include an improved voxelisation and extension to further use cases (e.g. particle types not yet covered), while for FastCaloGANtainer they concern its deployment on a wider variety of resources with multi-CPU/GPU nodes and different architectures (including cutting-edge HPC clusters such as Leonardo at CINECA in Bologna, Italy).</td></tr> </table> <br/> <br/><br/><div align="right"><div style="padding-bottom:2px;padding-top:30px;"><span class="moreinfo" style="margin-right:10px;"> <a href="" class="moreinfo">Back to search</a> </span></div></div> <div class="bottom-left-folded"><div class="recordlastmodifiedbox" style="position:relative;margin-left:1px"> Record created 2024-11-27, last modified 2024-11-27</div></div> <div class="bottom-right-folded" style="text-align:right;padding-bottom:2px;"> <span class="moreinfo" style="margin-right:10px;"><a href="/search?ln=en&p=recid%3A2918115&rm=wrd" class="moreinfo">Similar records</a></span></div> </div> </div> </div> <br/> <br /> <div class="detailedrecordminipanel"> <div class="top-left"></div><div class="top-right"></div> <div class="inside"> <div id="detailedrecordminipanelfile" style="width:33%;float:left;text-align:center;margin-top:0"> <div><small class="detailedRecordActions">Fulltext:</small> <br /><a href="/record/2918115/files/ATL-SOFT-SLIDE-2024-600.pdf"><img style="border:none" src="/img/file-icon-text-12x16.gif" alt="Download fulltext"/>PDF</a><br /></div><small class="detailedRecordActions">External link:</small><br /><small><a href="https://cds.cern.ch/record/2914713"><img style="border:none" src="/img/file-icon-text-12x16.gif" alt="Download fulltext"/>Original Communication (restricted to ATLAS)</a></small> </div> <div id="detailedrecordminipanelreview" style="width:30%;float:left;text-align:center"> </div> <div id="detailedrecordminipanelactions" style="width:36%;float:right;text-align:right;"> <ul class="detailedrecordactions"> <li><a href="/yourbaskets/add?ln=en&recid=2918115">Add to personal basket</a></li> <li>Export as <a style="text-decoration:underline;font-weight:normal" href="/record/2918115/export/hx?ln=en">BibTeX</a>, <a style="text-decoration:underline;font-weight:normal" href="/record/2918115/export/hm?ln=en">MARC</a>, <a style="text-decoration:underline;font-weight:normal" href="/record/2918115/export/xm?ln=en">MARCXML</a>, <a style="text-decoration:underline;font-weight:normal" href="/record/2918115/export/xd?ln=en">DC</a>, <a style="text-decoration:underline;font-weight:normal" href="/record/2918115/export/xe?ln=en">EndNote</a>,  <a style="text-decoration:underline;font-weight:normal" href="/record/2918115/export/xn?ln=en">NLM</a>, <a style="text-decoration:underline;font-weight:normal" href="/record/2918115/export/xw?ln=en">RefWorks</a> </li> </ul> <div style='padding-left: 13px;'>  <div id="bookmark"></div> <div id="bookmark_sciencewise"></div> <style type="text/css"> #bookmark_sciencewise, #bookmark {float: left;} #bookmark_sciencewise li {padding: 2px; width: 25px;} #bookmark_sciencewise ul, #bookmark ul {list-style-image: none;} </style> <script type="text/javascript" src="/js/jquery.bookmark.min.js"></script> <style type="text/css">@import "/css/jquery.bookmark.css";</style> <script type="text/javascript">// <![CDATA[ $.bookmark.addSite('sciencewise', 'ScienceWise.info', 'https://cds.cern.ch/img/sciencewise.png', 'en', 'bookmark', 'http://sciencewise.info/bookmarks/cds:2918115/add'); $('#bookmark_sciencewise').bookmark({sites: ['sciencewise']}); $('#bookmark').bookmark({ sites: ['facebook', 'twitter', 'linkedin', 'google_plusone'], icons: '/img/bookmarks.png', url: 'https://cds.cern.ch/record/2918115', addEmail: true, title: "Recent Advances in the GAN-Based Fast Calorimeter Simulation of the ATLAS Experiment", description: "Simulation of the detector response is a major computational challenge in modern High-Energy Physics experiments, accounting for about 40% of the total computational resources used in ATLAS. The simulation of the calorimeter response is particularly demanding, consuming about 80% of the total simulation time. In order to make the best use of the available computational resources, fast simulation tools based on Machine Learning techniques have been developed to simulate the calorimeter response faster than Geant4 while maintaining a high level of accuracy. One such tool, developed by the ATLAS Collaboration and currently in production for LHC Run 3, is FastCaloGAN, which uses Generative Adversarial Networks (GANs) to generate electromagnetic and hadronic showers. To facilitate the training and optimisation of the GANs, and to enable a more efficient use of computational resources, a container-based system, FastCaloGANtainer, facilitates the deployment of the FastCaloGAN training on complementary high-performance resources such as High Performance Computing (HPC) farms and ensures its operational independence from the underlying system. This poster presents the latest developments in FastCaloGAN and FastCaloGANtainer, discussing their technical details and recent improvements in terms of Physics and computational performance. For FastCaloGAN, these improvements include an improved voxelisation and extension to further use cases (e.g. particle types not yet covered), while for FastCaloGANtainer they concern its deployment on a wider variety of resources with multi-CPU/GPU nodes and different architectures (including cutting-edge HPC clusters such as Leonardo at CINECA in Bologna, Italy)." }); // ]]> </script>  </div> </div> <div style="clear:both;margin-bottom: 0;"></div> </div> <div class="bottom-left"></div><div class="bottom-right"></div> </div> </div></div> <footer id="footer" class="pagefooter clearfix">  <div class="pagefooterstripeleft"> CERN Document Server :: <a class="footer" href="https://cds.cern.ch/?ln=en">Search</a> :: <a class="footer" href="https://cds.cern.ch/submit?ln=en">Submit</a> :: <a class="footer" href="https://cds.cern.ch/youraccount/display?ln=en">Personalize</a> :: <a class="footer" href="https://cds.cern.ch/help/?ln=en">Help</a> :: <a class="footer" href="https://cern.service-now.com/service-portal?id=privacy_policy&se=CDS-Service" target="_blank">Privacy Notice</a> <br /> Powered by <a class="footer" href="http://invenio-software.org/">Invenio</a> <br /> Maintained by <a class="footer" href="https://cern.service-now.com/service-portal?id=service_element&name=CDS-Service">CDS Service</a> - Need help? 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