Physics Community Needs, Tools, and Resources for Machine Learning
Philip Harris, Erik Katsavounidis, William Patrick McCormack, Dylan Rankin, Yongbin Feng, Abhijith Gandrakota, Christian Herwig, Burt Holzman, Kevin Pedro, Nhan Tran, Tingjun Yang, Jennifer Ngadiuba
arXiv:2203.16255v15 citations
Originality Synthesis-oriented
AI Analysis
It identifies resource gaps for physicists using ML, but is incremental as it synthesizes existing issues without new solutions.
The paper addresses the computational challenges of machine learning in physics research by discussing community needs, tools, and resources for optimizing latency and throughput.
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.