Year: 2019
Type: article
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 and some examples, we describe how Merlin was deployed on the Sierra Supercomputer at Lawrence Livermore National Laboratory to create an unprecedented benchmark inertial confinement fusion dataset of approximately 100 million individual simulations and over 24 terabytes of multi-modal physics-based scalar, vector and hyperspectral image data.
Authors: J. L. Peterson, Rushil Anirudh, Kevin Athey, Benjamin Bay, Peer‐Timo Bremer, Vic Castillo, Francesco Di Natale, David L. Fox, Jim Gaffney, David Hysom, Sam Adé Jacobs, Bhavya Kailkhura, J. M. Koning, Bogdan Kustowski, S. H. Langer, Peter Robinson, Jessica Semler, B. K. Spears, Jayaraman J. Thiagarajan, Brian Van Essen, Jae-Seung Yeom
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Awards: G14554683, G2145270357, G991281326
OpenAlex ID: W2992725702