Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

Francis J. Alexander; James Ang; Jenna A. Bilbrey; Jan Balewski; Tiernan Casey; Ryan Chard; Jong Choi; Sutanay Choudhury; Bert Debusschere; Anthony M. DeGennaro; Nikoli Dryden; J. Austin Ellis; Ian Foster; Cristina Garcia Cardona; Sayan Ghosh; Peter Harrington; Yunzhi Huang; Shantenu Jha; Travis Johnston; Ai Kagawa; Ramakrishnan Kannan; Neeraj Kumar; Zhengchun Liu; Naoya Maruyama; Satoshi Matsuoka; Erin McCarthy; Jamaludin Mohd-Yusof; Peter Nugent; Yosuke Oyama; Thomas Proffen; David Pugmire; Sivasankaran Rajamanickam; Vinay Ramakrishniah; Malachi Schram; Sudip K. Seal; Ganesh Sivaraman; Christine Sweeney; Li Tan; Rajeev Thakur; Brian Van Essen; Logan Ward; Paul Welch; Michael Wolf; Sotiris S. Xantheas; Kevin G. Yager; Shinjae Yoo; Byung Jun Yoon

doi:10.1177/10943420211029302

Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

Francis J. Alexander
, James Ang
, Jenna A. Bilbrey
, Jan Balewski
, Tiernan Casey
, Ryan Chard
, Jong Choi
, Sutanay Choudhury
, Bert Debusschere
, Anthony M. DeGennaro
, Nikoli Dryden
, J. Austin Ellis
, Ian Foster
, Cristina Garcia Cardona
, Sayan Ghosh
, Peter Harrington
, Yunzhi Huang
, Shantenu Jha
, Travis Johnston
, Ai Kagawa

Ramakrishnan Kannan, Neeraj Kumar, Zhengchun Liu, Naoya Maruyama, Satoshi Matsuoka, Erin McCarthy, Jamaludin Mohd-Yusof, Peter Nugent, Yosuke Oyama, Thomas Proffen, David Pugmire, Sivasankaran Rajamanickam, Vinay Ramakrishniah, Malachi Schram, Sudip K. Seal, Ganesh Sivaraman, Christine Sweeney, Li Tan, Rajeev Thakur, Brian Van Essen, Logan Ward, Paul Welch, Michael Wolf, Sotiris S. Xantheas, Kevin G. Yager, Shinjae Yoo, Byung Jun Yoon

Department of Materials Design and Innovation

Brookhaven National Laboratory
Pacific Northwest National Laboratory
Lawrence Berkeley National Laboratory
Sandia National Laboratories, New Mexico
Argonne National Laboratory
Oak Ridge National Laboratory
Lawrence Livermore National Laboratory
Swiss Federal Institute of Technology Zurich
RIKEN
Institute of Science Tokyo
University of Oregon
Los Alamos National Laboratory

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

Abstract

Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.

Original language	English
Pages (from-to)	598-616
Number of pages	19
Journal	International Journal of High Performance Computing Applications
Volume	35
Issue number	6
DOIs	https://doi.org/10.1177/10943420211029302
State	Published - Nov 2021

Keywords

Machine learning
active learning
exascale computing
high-performance computing for machine learning
machine learning for high-performance computing
reinforcement learning

Access to Document

10.1177/10943420211029302

Cite this

Alexander, F. J., Ang, J., Bilbrey, J. A., Balewski, J., Casey, T., Chard, R., Choi, J., Choudhury, S., Debusschere, B., DeGennaro, A. M., Dryden, N., Ellis, J. A., Foster, I., Cardona, C. G., Ghosh, S., Harrington, P., Huang, Y., Jha, S., Johnston, T., ... Yoon, B. J. (2021). Co-design Center for Exascale Machine Learning Technologies (ExaLearn). International Journal of High Performance Computing Applications, 35(6), 598-616. https://doi.org/10.1177/10943420211029302

@article{ee31364fefb7419e9426594d3384d47d,

title = "Co-design Center for Exascale Machine Learning Technologies (ExaLearn)",

abstract = "Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.",

keywords = "Machine learning, active learning, exascale computing, high-performance computing for machine learning, machine learning for high-performance computing, reinforcement learning",

author = "Alexander, \{Francis J.\} and James Ang and Bilbrey, \{Jenna A.\} and Jan Balewski and Tiernan Casey and Ryan Chard and Jong Choi and Sutanay Choudhury and Bert Debusschere and DeGennaro, \{Anthony M.\} and Nikoli Dryden and Ellis, \{J. Austin\} and Ian Foster and Cardona, \{Cristina Garcia\} and Sayan Ghosh and Peter Harrington and Yunzhi Huang and Shantenu Jha and Travis Johnston and Ai Kagawa and Ramakrishnan Kannan and Neeraj Kumar and Zhengchun Liu and Naoya Maruyama and Satoshi Matsuoka and Erin McCarthy and Jamaludin Mohd-Yusof and Peter Nugent and Yosuke Oyama and Thomas Proffen and David Pugmire and Sivasankaran Rajamanickam and Vinay Ramakrishniah and Malachi Schram and Seal, \{Sudip K.\} and Ganesh Sivaraman and Christine Sweeney and Li Tan and Rajeev Thakur and \{Van Essen\}, Brian and Logan Ward and Paul Welch and Michael Wolf and Xantheas, \{Sotiris S.\} and Yager, \{Kevin G.\} and Shinjae Yoo and Yoon, \{Byung Jun\}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2021.",

year = "2021",

month = nov,

doi = "10.1177/10943420211029302",

language = "English",

volume = "35",

pages = "598--616",

journal = "International Journal of High Performance Computing Applications",

issn = "1094-3420",

publisher = "SAGE Publications Inc.",

number = "6",

}

Alexander, FJ, Ang, J, Bilbrey, JA, Balewski, J, Casey, T, Chard, R, Choi, J, Choudhury, S, Debusschere, B, DeGennaro, AM, Dryden, N, Ellis, JA, Foster, I, Cardona, CG, Ghosh, S, Harrington, P, Huang, Y, Jha, S, Johnston, T, Kagawa, A, Kannan, R, Kumar, N, Liu, Z, Maruyama, N, Matsuoka, S, McCarthy, E, Mohd-Yusof, J, Nugent, P, Oyama, Y, Proffen, T, Pugmire, D, Rajamanickam, S, Ramakrishniah, V, Schram, M, Seal, SK, Sivaraman, G, Sweeney, C, Tan, L, Thakur, R, Van Essen, B, Ward, L, Welch, P, Wolf, M, Xantheas, SS, Yager, KG, Yoo, S & Yoon, BJ 2021, 'Co-design Center for Exascale Machine Learning Technologies (ExaLearn)', International Journal of High Performance Computing Applications, vol. 35, no. 6, pp. 598-616. https://doi.org/10.1177/10943420211029302

TY - JOUR

T1 - Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

AU - Alexander, Francis J.

AU - Ang, James

AU - Bilbrey, Jenna A.

AU - Balewski, Jan

AU - Casey, Tiernan

AU - Chard, Ryan

AU - Choi, Jong

AU - Choudhury, Sutanay

AU - Debusschere, Bert

AU - DeGennaro, Anthony M.

AU - Dryden, Nikoli

AU - Ellis, J. Austin

AU - Foster, Ian

AU - Cardona, Cristina Garcia

AU - Ghosh, Sayan

AU - Harrington, Peter

AU - Huang, Yunzhi

AU - Jha, Shantenu

AU - Johnston, Travis

AU - Kagawa, Ai

AU - Kannan, Ramakrishnan

AU - Kumar, Neeraj

AU - Liu, Zhengchun

AU - Maruyama, Naoya

AU - Matsuoka, Satoshi

AU - McCarthy, Erin

AU - Mohd-Yusof, Jamaludin

AU - Nugent, Peter

AU - Oyama, Yosuke

AU - Proffen, Thomas

AU - Pugmire, David

AU - Rajamanickam, Sivasankaran

AU - Ramakrishniah, Vinay

AU - Schram, Malachi

AU - Seal, Sudip K.

AU - Sivaraman, Ganesh

AU - Sweeney, Christine

AU - Tan, Li

AU - Thakur, Rajeev

AU - Van Essen, Brian

AU - Ward, Logan

AU - Welch, Paul

AU - Wolf, Michael

AU - Xantheas, Sotiris S.

AU - Yager, Kevin G.

AU - Yoo, Shinjae

AU - Yoon, Byung Jun

PY - 2021/11

Y1 - 2021/11

N2 - Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.

AB - Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities.

KW - Machine learning

KW - active learning

KW - exascale computing

KW - high-performance computing for machine learning

KW - machine learning for high-performance computing

KW - reinforcement learning

UR - https://www.scopus.com/pages/publications/85116052026

U2 - 10.1177/10943420211029302

DO - 10.1177/10943420211029302

M3 - Article

AN - SCOPUS:85116052026

SN - 1094-3420

VL - 35

SP - 598

EP - 616

JO - International Journal of High Performance Computing Applications

JF - International Journal of High Performance Computing Applications

IS - 6

ER -

Co-design Center for Exascale Machine Learning Technologies (ExaLearn)

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Keywords

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