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Repurposing of the Run 2 CMS High Level Trigger Infrastructure as a Cloud Resource for Offline Computing / CMS Collaboration
The former CMS Run 2 High Level Trigger (HLT) farm is one of the largest contributors to CMS compute resources, providing about 25k job slots for offline computing. This CPU farm was initially employed as an opportunistic resource, exploited during inter-fill periods, in the LHC Run 2. [...]
arXiv:2405.14639.- 2024 - 8 p. - Published in : EPJ Web Conf.: 295 (2024) , pp. 03036 Fulltext: 439698769ecbc6a4d64475c1a6ab479e - PDF; document - PDF; 2405.14639 - PDF; External link: Fermilab Library Server
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.03036

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Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing / Cai, Tejin (York U., Canada) ; Herner, Kenneth (Fermilab) ; Yang, Tingjun (Fermilab) ; Wang, Michael (Fermilab) ; Flechas, Maria Acosta (Fermilab) ; Harris, Philip (MIT) ; Holzman, Burt (Fermilab) ; Pedro, Kevin (Fermilab) ; Tran, Nhan (Fermilab)
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. [...]
arXiv:2301.04633; FERMILAB-PUB-22-944-ND-PPD-SCD.- 2023 - 13 p. - Published in : Comput. Softw. Big Sci. 7 (2023) 11 Fulltext: 2301.04633 - PDF; Publication - PDF; FERMILAB-PUB-22-944-ND-PPD-SCD - PDF; External link: Fermilab Accepted Manuscript

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Adoption of a token-based authentication model for the CMS Submission Infrastructure. / Pérez-Calero Yzquierdo, Antonio (Madrid, CIEMAT ; PIC, Bellaterra) ; Mascheroni, Marco (UC, San Diego) ; Kizinevic, Edita (CERN) ; Khan, Farrukh Aftab (Fermilab) ; Kim, Hyunwoo (Fermilab) ; Flechas, Maria Acosta (Fermilab) ; Tsipinakis, Nikos (CERN) ; Haleem, Saqib (NCP, Islamabad) ; Würthwein, Frank (UC, San Diego) /CMS Collaboration
The CMS Submission Infrastructure (SI) is the main computing resource provisioning system for CMS workloads. A number of HTCondor pools are employed to manage this infrastructure, which aggregates geographically distributed resources from the WLCG and other providers. [...]
arXiv:2405.14644.- Geneva : CERN, 2024 - 6 p. - Published in : EPJ Web Conf. 295 (2024) 04003 Fulltext: 2405.14644 - PDF; CR2023_170 - PDF; 24a38f66866e5065c5b8cfa45016bee6 - PDF; document - PDF; External link: Fermilab Library Server
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.04003

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The integration of heterogeneous resources in the CMS Submission Infrastructure for the LHC Run 3 and beyond / Pérez-Calero Yzquierdo, Antonio (Madrid, CIEMAT ; PIC, Bellaterra) ; Mascheroni, Marco (UC, San Diego) ; Kizinevic, Edita (CERN) ; Khan, Farrukh Aftab (Fermilab) ; Kim, Hyunwoo (Fermilab) ; Flechas, Maria Acosta (Fermilab) ; Tsipinakis, Nikos (CERN) ; Haleem, Saqib (NCP, Islamabad) /CMS Collaboration
While the computing landscape supporting LHC experiments is currently dominated by x86 processors at WLCG sites, this configuration will evolve in the coming years. LHC collaborations will be increasingly employing HPC and Cloud facilities to process the vast amounts of data expected during the LHC Run 3 and the future HL-LHC phase. [...]
arXiv:2405.14647.- Geneva : CERN, 2024 - 8 p. - Published in : EPJ Web Conf. 295 (2024) 04046 Fulltext: jt - PDF; 2405.14647 - PDF; CR2023_169 - PDF; document - PDF; External link: Fermilab Library Server
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.04046

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HPC resources for CMS offline computing: an integration and scalability challenge for the Submission Infrastructure / Pérez-Calero Yzquierdo, Antonio (Madrid, CIEMAT ; PIC, Bellaterra) ; Mascheroni, Marco (UC, San Diego) ; Kizinevic, Edita (CERN) ; Khan, Farrukh Aftab (Fermilab) ; Kim, Hyunwoo (Fermilab) ; Flechas, Maria Acosta (Fermilab) ; Tsipinakis, Nikos (CERN) ; Haleem, Saqib (NCP, Islamabad) /CMS Collaboration
The computing resource needs of LHC experiments are expected to continue growing significantly during the Run 3 and into the HL-LHC era. The landscape of available resources will also evolve, as HPC and Cloud resources will provide a comparable, or even dominant, fraction of the total compute capacity. [...]
arXiv:2405.14631.- Geneva : CERN, 2024 - 8 p. - Published in : EPJ Web Conf. 295 (2024) 01035 Fulltext: 2405.14631 - PDF; document - PDF; CR2023_168 - PDF; 5f90e54777e503471736fb7a0d539fca - PDF; External link: Fermilab Library Server
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.01035

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Applications and Techniques for Fast Machine Learning in Science / Deiana, Allison McCarn (Southern Methodist U.) ; Tran, Nhan (Fermilab ; Northwestern U. (main)) ; Agar, Joshua (Lehigh U. (main)) ; Blott, Michaela (Xilinx, Dublin) ; Di Guglielmo, Giuseppe (Columbia U. (main)) ; Duarte, Javier (UC, San Diego) ; Harris, Philip (MIT) ; Hauck, Scott (George Washington U. (main)) ; Liu, Mia (Purdue U.) ; Neubauer, Mark S. (Illinois U., Urbana) et al.
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. [...]
arXiv:2110.13041; FERMILAB-PUB-21-502-AD-E-SCD.- 2022-04-12 - 56 p. - Published in : Front. Big Data 5 (2022) 787421 Fulltext: 2110.13041 - PDF; fermilab-pub-21-502-ad-e-scd - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server

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