학술논문
Physics Community Needs, Tools, and Resources for Machine Learning
Document Type
Working Paper
Author
Harris, Philip; Katsavounidis, Erik; McCormack, William Patrick; Rankin, Dylan; Feng, Yongbin; Gandrakota, Abhijith; Herwig, Christian; Holzman, Burt; Pedro, Kevin; Tran, Nhan; Yang, Tingjun; Ngadiuba, Jennifer; Coughlin, Michael; Hauck, Scott; Hsu, Shih-Chieh; Khoda, Elham E; Chen, Deming; Neubauer, Mark; Duarte, Javier; Karagiorgi, Georgia; Liu, Mia
Source
Subject
Language
Abstract
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utilized and accessed in the coming years.
Comment: Contribution to Snowmass 2021, 33 pages, 5 figures
Comment: Contribution to Snowmass 2021, 33 pages, 5 figures