Krupa, Jeffrey and Lin, Kelvin and Acosta Flechas, Maria and Dinsmore, Jack and Duarte, Javier and Harris, Philip and Hauck, Scott and Holzman, Burt and Hsu, Shih-Chieh and Klijnsma, Thomas and Liu, Mia and Pedro, Kevin and Rankin, Dylan and Suaysom, Natchanon and Trahms, Matt and Tran, Nhan (2021) GPU coprocessors as a service for deep learning inference in high energy physics. Machine Learning: Science and Technology, 2 (3). 035005. ISSN 2632-2153
Krupa_2021_Mach._Learn.__Sci._Technol._2_035005.pdf - Published Version
Download (948kB)
Abstract
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.
Item Type: | Article |
---|---|
Subjects: | Middle East Library > Multidisciplinary |
Depositing User: | Unnamed user with email support@middle-eastlibrary.com |
Date Deposited: | 17 Jul 2023 05:48 |
Last Modified: | 05 Sep 2024 11:33 |
URI: | http://editor.openaccessbook.com/id/eprint/1256 |