Co-design Center for Exascale Machine Learning Technologies (ExaLearn)
| Autor: | Shinjae Yoo, Logan Ward, Nikoli Dryden, Ramakrishnan Kannan, Rajeev Thakur, Bert Debusschere, Ganesh Sivaraman, Sutanay Choudhury, Zhengchun Liu, Neeraj Kumar, Peter Nugent, Francis J. Alexander, Sudip K. Seal, Shantenu Jha, James A. Ang, David Pugmire, Li Tan, Ian Foster, Yunzhi Huang, Paul M. Welch, Cristina Garcia Cardona, Sivasankaran Rajamanickam, Thomas Proffen, Ai Kagawa, Malachi Schram, Byung-Jun Yoon, Jamaludin Mohd-Yusof, Erin McCarthy, Tiernan Casey, Sotiris S. Xantheas, Vinay Ramakrishniah, Jan Balewski, Sayan Ghosh, Brian Van Essen, Michael M. Wolf, Christine Sweeney, J. Austin Ellis, Peter Harrington, Jong Choi, Yosuke Oyama, Naoya Maruyama, Satoshi Matsuoka, Jenna A. Bilbrey, Kevin G. Yager, Anthony M. DeGennaro, Travis Johnston, Ryan Chard |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Co-design
ComputerSystemsOrganization_COMPUTERSYSTEMIMPLEMENTATION Active learning (machine learning) Statistical learning Computer science business.industry Machine learning computer.software_genre Exascale computing Theoretical Computer Science Hardware and Architecture Reinforcement learning Center (algebra and category theory) Artificial intelligence business computer Software |
| Zdroj: | The International Journal of High Performance Computing Applications. 35:598-616 |
| ISSN: | 1741-2846 1094-3420 |
| DOI: | 10.1177/10943420211029302 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|