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Title: Potential Ideas for an El Capitan Center of Excellence (COE) Around Intelligent Simulation

Abstract

Proposing a Center of Excellence under the CORAL-2 NRE is a mandatory requirement. The current Sierra COE (CORAL-1) is largely focused on helping LLNL applications make the disruptive transition to a heterogeneous GPU-based system by 2018. We expect a continuation of COE activities around optimizing our broad and diverse application base (of so-called “traditional” simulation codes) optimized for the El Capitan architecture, as well as supporting the underlying software stack (compilers, tools, programming models, etc.) – but do not expect this to require as much effort in the El Capitan COE (assuming a heterogeneous node architecture). What follows are some thoughts on 3 potential topics of interest for an El Capitan COE at LLNL – largely focused around AI and machine learning and the concept of advancing our goal of intelligent simulation or cognitive computing in the timeframe of deployment and production use of El Capitan (2023-2038). We believe vendor engagement through a COE in this area will provide a natural point-of-interest between LLNL and our vendor partner for common advancement of machine learning capabilities focused on scientific data – potentially greatly broadening the ecosystem around HPC architectures and the supporting software stack for scientific simulation-based AI.

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1438616
Report Number(s):
LLNL-TR-748599
DOE Contract Number:  
AC52-07NA27344
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE

Citation Formats

Spears, B., Van Essen, B., Clouse, C., Neely, R., and McCoy, M.. Potential Ideas for an El Capitan Center of Excellence (COE) Around Intelligent Simulation. United States: N. p., 2018. Web. doi:10.2172/1438616.
Spears, B., Van Essen, B., Clouse, C., Neely, R., & McCoy, M.. Potential Ideas for an El Capitan Center of Excellence (COE) Around Intelligent Simulation. United States. doi:10.2172/1438616.
Spears, B., Van Essen, B., Clouse, C., Neely, R., and McCoy, M.. Fri . "Potential Ideas for an El Capitan Center of Excellence (COE) Around Intelligent Simulation". United States. doi:10.2172/1438616. https://www.osti.gov/servlets/purl/1438616.
@article{osti_1438616,
title = {Potential Ideas for an El Capitan Center of Excellence (COE) Around Intelligent Simulation},
author = {Spears, B. and Van Essen, B. and Clouse, C. and Neely, R. and McCoy, M.},
abstractNote = {Proposing a Center of Excellence under the CORAL-2 NRE is a mandatory requirement. The current Sierra COE (CORAL-1) is largely focused on helping LLNL applications make the disruptive transition to a heterogeneous GPU-based system by 2018. We expect a continuation of COE activities around optimizing our broad and diverse application base (of so-called “traditional” simulation codes) optimized for the El Capitan architecture, as well as supporting the underlying software stack (compilers, tools, programming models, etc.) – but do not expect this to require as much effort in the El Capitan COE (assuming a heterogeneous node architecture). What follows are some thoughts on 3 potential topics of interest for an El Capitan COE at LLNL – largely focused around AI and machine learning and the concept of advancing our goal of intelligent simulation or cognitive computing in the timeframe of deployment and production use of El Capitan (2023-2038). We believe vendor engagement through a COE in this area will provide a natural point-of-interest between LLNL and our vendor partner for common advancement of machine learning capabilities focused on scientific data – potentially greatly broadening the ecosystem around HPC architectures and the supporting software stack for scientific simulation-based AI.},
doi = {10.2172/1438616},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Mar 23 00:00:00 EDT 2018},
month = {Fri Mar 23 00:00:00 EDT 2018}
}

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