GPU coprocessors as a service for deep learning inference in high energy physics
Name
Krupa_2021_Mach._Learn.__Sci._Technol._2_035005.pdf
Description
Published version
Size
921.89 KB
Format
Adobe PDF
Checksum (MD5)
b1545412eff36db586189026265bd082
Author(s)
Krupa, Jeffrey
Lin, Kelvin
Acosta Flechas, Maria
Dinsmore, Jack
Duarte, Javier
Harris, Philip
Hauck, Scott
Holzman, Burt
Hsu, Shih-Chieh
Klijnsma, Thomas
Date Issued
2021
Journal
Machine Learning: Science and Technology
Publisher
IOP Publishing
Citation
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).
Version
Final published version
Abstract
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.
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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1088/2632-2153/ABEC21
