Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

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dc.contributor.authorHayrapetyan, A.
dc.contributor.authorTumasyan, A.
dc.contributor.authorAdam, W.
dc.contributor.authorAndrejkovic, J.W.
dc.contributor.authorBergauer, T.
dc.contributor.authorChatterjee, S.
dc.contributor.authorDamanakis, K.
dc.date.accessioned2025-01-12T17:08:49Z
dc.date.available2025-01-12T17:08:49Z
dc.date.issued2024
dc.departmentKaramanoğlu Mehmetbey Üniversitesi
dc.description.abstractComputing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors. © The Author(s) 2024.
dc.description.sponsorshipCouncil of Scientific and Industrial Research, India, CSIR; Ministry of Business, Innovation and Employment, MBIE; Ministry of Education and Science, MES; Benemérita Universidad Autónoma de Puebla, BUAP; Department of Atomic Energy, Government of India, DAE; PCTI; National Academy of Sciences of Ukraine, NASU; National Science and Technology Development Agency, ????; National Research Foundation of Korea, NRF; MSES; Ministerio de Educación, Cultura y Deporte, MECD; National Science Foundation, NSF; Institut National de Physique Nucléaire et de Physique des Particules, IN2P3; Latvijas Zin?tnes Padome; Science and Technology Facilities Council, STFC; Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, CINVESTAV; Ministry of Science, ICT and Future Planning, MSIP; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, FAPERJ; Ministerio de Ciencia e Innovación, MCIN; Universiti Malaya, UM; Ministry of Science and Technology, Taiwan, MOST; Hellenic Foundation for Research and Innovation, ??.??.?.?; National Science Council, NSC; Ministry of Science,Technology and Research, MoSTR; Hispanics in Philanthropy, HIP; Instituto Nazionale di Fisica Nucleare, INFN; Secretaría de Educación Pública, SEP; Austrian Science Fund, FWF; Department of Science and Technology, Ministry of Science and Technology, India, DST; Chulalongkorn Academic; Consejo Nacional de Humanidades, Ciencias y Tecnologías, Conahcyt; Belgian Federal Science Policy Office, BELSPO; Centre National de la Recherche Scientifique, CNRS; Bundesministerium für Bildung und Forschung, BMBF; Fonds Wetenschappelijk Onderzoek, FWO; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK; Kavli Foundation; Helmholtz-Gemeinschaft, HGF; Commissariat à l'Énergie Atomique et aux Énergies Alternatives, CEA; Research Council of Finland, AKA; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Pakistan Atomic Energy Commission, PAEC; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; Türkiye Enerji, Nükleer ve Maden Araştırma Kurumu, TENMAK; Ministry of Education - Singapore, MOE; European Commission, EC; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR; Science Foundation Ireland, SFI; U.S. Department of Energy, USDOE; International Council of Shopping Centers, ICSC; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; Cosmetic Surgery Foundation, CSF; Agencia Estatal de Investigación, AEI; Programa Severo Ochoa del Principado de Asturias; General Secretariat for Research and Innovation, GSRI; Bulgarian National Science Fund, BNSF; Direktion für Entwicklung und Zusammenarbeit, DEZA; Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie, BMBWF; Ministerio de Ciencia, Tecnología e Innovación; Alfred P. Sloan Foundation, APSF; Maryland Ornithological Society, MOS; Chinese Academy of Sciences, CAS; Ministry of Higher Education, Science, Research and Innovation, Thailand, MHESRI; Fonds De La Recherche Scientifique - FNRS, FNRS; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, FAPERGS; Weston Havens Foundation; Institute for Research in Fundamental Sciences, IPM; Magyar Tudományos Akadémia, MTA; A.G. Leventis Foundation; Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture, FRIA; Laboratorio Nacional de Supercómputo del Sureste de Mexico, LNS; European Regional Development Fund, ERDF; Nvidia; CERN; Louisiana Academy of Sciences, LAS; Ministerstvo Školství, Mláde?e a T?lov?chovy, MŠMT; Secretaría de Educación Superior, Ciencia, Tecnología e Innovación, SENESCYT; Fundamental Research Funds for the Central Universities; Agentschap voor Innovatie door Wetenschap en Technologie, IWT; Universidad Autónoma de San Luis Potosí, UASLP; National Natural Science Foundation of China, NSFC; Beijing Municipal Science and Technology Commission, Adminitrative Commission of Zhongguancun Science Park, (Z191100007219010); Beijing Municipal Science and Technology Commission, Adminitrative Commission of Zhongguancun Science Park; Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, (MDM-2017-0765); National Science, Research and Innovation Fund, (B37G660013); Excellence of Science, (30820817); Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, NKFIH, (K 131991, K 133046, K 143477, K 124845, K 129058, K 143460, K 128713, K 124850, TKP2021-NKTA-64, 20202.2.1-ED-2021-00181, K 138136, K 128786); Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, NKFIH; European Research Council, ERC, (TK202); European Research Council, ERC; Shota Rustaveli National Science Foundation, SRNSF, (FR-22-985); Shota Rustaveli National Science Foundation, SRNSF; Alexander von Humboldt-Stiftung, AvH, (22rl-037); Alexander von Humboldt-Stiftung, AvH; Deutsche Forschungsgemeinschaft, DFG, (400140256—GRK2497, 390833306); Deutsche Forschungsgemeinschaft, DFG; Horizon 2020, (724704, 758316, 824093, 765710, 752730, 675440); Horizon 2020; European Cooperation in Science and Technology, COST, (CA16108); European Cooperation in Science and Technology, COST; Welch Foundation, (C-1845); Welch Foundation; Narodowe Centrum Nauki, NCN, (2021/43/B/ST2/01552, 2021/41/B/ST2/01369); Narodowe Centrum Nauki, NCN; Ministry of Education and Science, (2022/WK/14); Ministry of Education and Science; Fundação para a Ciência e a Tecnologia, FCT, (CEECIND/01334/2018); Fundação para a Ciência e a Tecnologia, FCT; Qatar National Research Fund, QNRF, (MCIN/AEI/10.13039/501100011033); Qatar National Research Fund, QNRF
dc.identifier.doi10.1007/s41781-024-00124-1
dc.identifier.issn2510-2044
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85203288230
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/11492/9046
dc.identifier.volume8
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.ispartofComputing and Software for Big Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20250111
dc.subjectCMS
dc.subjectMachine learning
dc.subjectOffline and computing
dc.titlePortable Acceleration of CMS Computing Workflows with Coprocessors as a Service
dc.typeArticle

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