GPU coprocessors as a service for deep learning inference in high energy physics
<jats:title>Abstract</jats:title> <jats:p>In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN Large Hadron Collider (LHC)...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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IOP Publishing
2022
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| Online Access: | https://hdl.handle.net/1721.1/142112 |
| _version_ | 1826216348150136832 |
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| author | Krupa, Jeffrey Lin, Kelvin Acosta Flechas, Maria Dinsmore, Jack Duarte, Javier Harris, Philip Hauck, Scott Holzman, Burt Hsu, Shih-Chieh Klijnsma, Thomas Liu, Mia Pedro, Kevin Rankin, Dylan Suaysom, Natchanon Trahms, Matt Tran, Nhan |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Krupa, Jeffrey Lin, Kelvin Acosta Flechas, Maria Dinsmore, Jack Duarte, Javier Harris, Philip Hauck, Scott Holzman, Burt Hsu, Shih-Chieh Klijnsma, Thomas Liu, Mia Pedro, Kevin Rankin, Dylan Suaysom, Natchanon Trahms, Matt Tran, Nhan |
| author_sort | Krupa, Jeffrey |
| collection | MIT |
| description | <jats:title>Abstract</jats:title>
<jats:p>In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN Large Hadron Collider (LHC), these two issues will confront one another as the collider is upgraded for high luminosity running. Alternative processors such as graphics processing units (GPUs) can resolve this confrontation provided that algorithms can be sufficiently accelerated. In many cases, algorithmic speedups are found to be largest through the adoption of deep learning algorithms. We present a comprehensive exploration of the use of GPU-based hardware acceleration for deep learning inference within the data reconstruction workflow of high energy physics. We present several realistic examples and discuss a strategy for the seamless integration of coprocessors so that the LHC can maintain, if not exceed, its current performance throughout its running.</jats:p> |
| first_indexed | 2024-09-23T16:45:54Z |
| format | Article |
| id | mit-1721.1/142112 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:45:54Z |
| publishDate | 2022 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1421122023-06-22T17:43:16Z GPU coprocessors as a service for deep learning inference in high energy physics Krupa, Jeffrey Lin, Kelvin Acosta Flechas, Maria Dinsmore, Jack Duarte, Javier Harris, Philip Hauck, Scott Holzman, Burt Hsu, Shih-Chieh Klijnsma, Thomas Liu, Mia Pedro, Kevin Rankin, Dylan Suaysom, Natchanon Trahms, Matt Tran, Nhan Massachusetts Institute of Technology. Department of Physics <jats:title>Abstract</jats:title> <jats:p>In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN Large Hadron Collider (LHC), these two issues will confront one another as the collider is upgraded for high luminosity running. Alternative processors such as graphics processing units (GPUs) can resolve this confrontation provided that algorithms can be sufficiently accelerated. In many cases, algorithmic speedups are found to be largest through the adoption of deep learning algorithms. We present a comprehensive exploration of the use of GPU-based hardware acceleration for deep learning inference within the data reconstruction workflow of high energy physics. We present several realistic examples and discuss a strategy for the seamless integration of coprocessors so that the LHC can maintain, if not exceed, its current performance throughout its running.</jats:p> 2022-04-26T18:25:15Z 2022-04-26T18:25:15Z 2021 2022-04-26T18:02:18Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142112 Krupa, Jeffrey, Lin, Kelvin, Acosta Flechas, Maria, Dinsmore, Jack, Duarte, Javier et al. 2021. "GPU coprocessors as a service for deep learning inference in high energy physics." Machine Learning: Science and Technology, 2 (3). en 10.1088/2632-2153/ABEC21 Machine Learning: Science and Technology Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf IOP Publishing IOP Publishing |
| spellingShingle | Krupa, Jeffrey Lin, Kelvin Acosta Flechas, Maria Dinsmore, Jack Duarte, Javier Harris, Philip Hauck, Scott Holzman, Burt Hsu, Shih-Chieh Klijnsma, Thomas Liu, Mia Pedro, Kevin Rankin, Dylan Suaysom, Natchanon Trahms, Matt Tran, Nhan GPU coprocessors as a service for deep learning inference in high energy physics |
| title | GPU coprocessors as a service for deep learning inference in high energy physics |
| title_full | GPU coprocessors as a service for deep learning inference in high energy physics |
| title_fullStr | GPU coprocessors as a service for deep learning inference in high energy physics |
| title_full_unstemmed | GPU coprocessors as a service for deep learning inference in high energy physics |
| title_short | GPU coprocessors as a service for deep learning inference in high energy physics |
| title_sort | gpu coprocessors as a service for deep learning inference in high energy physics |
| url | https://hdl.handle.net/1721.1/142112 |
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