scholarly journals How community software ecosystems can unlock the potential of exascale computing

2021 ◽  
Vol 1 (2) ◽  
pp. 92-94
Author(s):  
Lois Curfman McInnes ◽  
Michael A. Heroux ◽  
Erik W. Draeger ◽  
Andrew Siegel ◽  
Susan Coghlan ◽  
...  
Keyword(s):  
Software Ecosystems ◽  
Exascale Computing ◽  
Community Software

scholarly journals DTHMM ExaLB: discrete-time hidden Markov model for load balancing in distributed exascale computing environment

2020 ◽  
Vol 7 (1) ◽  
pp. 1743404
Author(s):  
Ulphat Bakhishoff ◽  
Ehsan Mousavi Khaneghah ◽  
Araz R. Aliev ◽  
Amirhossein Reyhani Showkatabadi ◽  
Stefania Tomasiello
Keyword(s):  
Load Balancing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Discrete Time ◽  
Hidden Markov ◽  
Computing Environment ◽  
Exascale Computing

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

2021 ◽  
pp. 109434202110293
Author(s):  
Francis J Alexander ◽  
James Ang ◽  
Jenna A Bilbrey ◽  
Jan Balewski ◽  
Tiernan Casey ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
New Technologies ◽  
Scientific Discovery ◽  
Language Translation ◽  
Learning Technologies ◽  
Department Of Energy ◽  
Experimental Science ◽  
Exascale Computing ◽  
Self Driving Cars

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.

scholarly journals The SAGE project: a storage centric approach for exascale computing

2018 ◽  
Author(s):  
Sai Narasimhamurthy ◽  
Nikita Danilov ◽  
Sining Wu ◽  
Ganesan Umanesan ◽  
Steven Wei Der Chien ◽  
...  
Keyword(s):  
Exascale Computing
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