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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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Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning
/ Shanahan, Phiala (MIT) ; Terao, Kazuhiro (SLAC) ; Whiteson, Daniel (UC, Irvine) ; Aarts, Gert (Swansea U. ; ECT, Trento ; Fond. Bruno Kessler, Trento) ; Adelmann, Andreas (Northeastern U. ; PSI, Villigen) ; Akchurin, N. (Texas Tech.) ; Alexandru, Andrei (George Washington U. ; Maryland U.) ; Amram, Oz (Johns Hopkins U.) ; Andreassen, Anders (Google Inc.) ; Apresyan, Artur (Fermilab) et al.
The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. [...]
arXiv:2209.07559 ; FERMILAB-CONF-22-719-ND-PPD-QIS-SCD.
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Fermilab Library Server - eConf - Fulltext - Fulltext
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FTS3: Data Movement Service in containers deployed in OKD
/ Lobato Pardavila, Lorena (Fermilab) ; Holzman, Burt (Fermilab) ; Karavakis, Edward (CERN) ; Bryant, Lincoln (Chicago U., EFI) ; Timm, Steven (Fermilab)
The File Transfer Service (FTS3) is a data movement service developed at CERN which is used to distribute the majority of the Large Hadron Collider’s data across the Worldwide LHC Computing Grid (WLCG) infrastructure. At Fermilab, we have deployed FTS3 instances for Intensity Frontier experiments (e.g. [...]
FERMILAB-CONF-21-053-SCD.-
2021 - 6 p.
- Published in : EPJ Web Conf. 251 (2021) 02058
Fulltext: document - PDF; fermilab-conf-21-053-scd - PDF;
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.02058
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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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FPGA-accelerated machine learning inference as a service for particle physics computing
/ Duarte, Javier (Fermilab) ; Harris, Philip (MIT) ; Hauck, Scott (Washington U., Seattle) ; Holzman, Burt (Fermilab) ; Hsu, Shih-Chieh (Washington U., Seattle) ; Jindariani, Sergo (Fermilab) ; Khan, Suffian (Microsoft, Redmond) ; Kreis, Benjamin (Fermilab) ; Lee, Brian (Microsoft, Redmond) ; Liu, Mia (Fermilab) et al.
New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. [...]
arXiv:1904.08986; FERMILAB-PUB-19-170-CD-CMS-E-ND.-
2019-10-14 - 16 p.
- Published in : Comput. Softw. Big Sci. 3 (2019) 13
Fulltext: 1904.08986 - PDF; fermilab-pub-19-170-cd-cms-e-nd - PDF; Fulltext from Publisher: PDF; External link: Fermilab Accepted Manuscript
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HEP Software Foundation Community White Paper Working Group -- Data Organization, Management and Access (DOMA)
/ Berzano, Dario (CERN) ; Bianchi, Riccardo Maria (Pittsburgh U.) ; Bird, Ian (CERN) ; Bockelman, Brian (Nebraska U.) ; Campana, Simone (CERN) ; De, Kaushik (Texas U., Arlington) ; Duellmann, Dirk (CERN) ; Elmer, Peter (Princeton U.) ; Gardner, Robert (Chicago U., EFI) ; Garonne, Vincent (Oslo U.) et al.
Without significant changes to data organization, management, and access (DOMA), HEP experiments will find scientific output limited by how fast data can be accessed and digested by computational resources. [...]
arXiv:1812.00761 ; HSF-CWP-2017-04 ; FERMILAB-PUB-18-671-CD.
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2018. - 18 p.
Fermilab Library Server (fulltext available) - Fulltext - Fulltext
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HPC resource integration into CMS Computing via HEPCloud
/ Hufnagel, Dirk (Fermilab) ; Holzman, Burt (Fermilab) ; Mason, David (Fermilab) ; Mhashilkar, Parag (Fermilab) ; Timm, Steven (Fermilab) ; Tiradani, Anthony (Fermilab) ; Khan, Farrukh Aftab (Fermilab) ; Gutsche, Oliver (Fermilab) ; Bloom, Kenneth (U. Nebraska, Lincoln)
The higher energy and luminosity from the LHC in Run 2 have put increased pressure on CMS computing resources. Extrapolating to even higher luminosities (and thus higher event complexities and trigger rates) beyond Run3, it becomes clear that simply scaling up the the current model of CMS computing alone will become economically unfeasible. [...]
CMS-CR-2018-283; FERMILAB-CONF-18-630-CD.-
Geneva : CERN, 2019 - 8 p.
- Published in : EPJ Web Conf. 214 (2019) 03031
CMS Note: PDF; Fulltext from publisher: PDF;
In : 23rd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2018, Sofia, Bulgaria, 9 - 13 Jul 2018, pp.03031
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