2019/02/27 by Geoffrey Fox, Fox, Geoffrey, James A. Glazier +23
Computer Science · Decision Sciences · Physics and Astronomy · #Advanced Data Storage Technologies #Computational Physics (physics.comp-ph) #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #FOS: Physical sciences #Parallel #Scientific Computing and Data Management #and Cluster Computing (cs.DC) #cs.DC #physics.comp-ph
paper · pdf · doi:10.48550/arxiv.1902.10810
arxiv created 2019/02/27 · openalex publication_date 2019/02/27 · arxiv updated 2019/03/01 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
The convergence of HPC and data-intensive methodologies provide a promising approach to major performance improvements. This paper provides a general description of the interaction between traditional HPC and ML approaches and motivates the Learning Everywhere paradigm for HPC. We introduce the concept of effective performance that one can achieve by combining learning methodologies with simulation-based approaches, and distinguish between traditional performance as measured by benchmark scores. To support the promise of integrating HPC and learning methods, this paper examines specific examples and opportunities across a series of domains. It concludes with a series of open computer science and cyberinfrastructure questions and challenges that the Learning Everywhere paradigm presents.