1. Enabling Machine Learning-Ready HPC Ensembles with Merlin
- Author
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J. Luc Peterson, Ben Bay, Joe Koning, Peter Robinson, Jessica Semler, Jeremy White, Rushil Anirudh, Kevin Athey, Peer-Timo Bremer, Francesco Di Natale, David Fox, Jim A. Gaffney, Sam A. Jacobs, Bhavya Kailkhura, Bogdan Kustowski, Steven Langer, Brian Spears, Jayaraman Thiagarajan, Brian Van Essen, and Jae-Seung Yeom
- Subjects
Plasma Physics (physics.plasm-ph) ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Networks and Communications ,Hardware and Architecture ,FOS: Physical sciences ,Distributed, Parallel, and Cluster Computing (cs.DC) ,Computational Physics (physics.comp-ph) ,Physics - Computational Physics ,Software ,Physics - Plasma Physics ,Machine Learning (cs.LG) - Abstract
With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. In this paper, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. In addition to its design, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19., Comment: 28 pages, 9 figures; Submitted to FGCS
- Published
- 2019
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