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Title: Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning

Authors:
 [1];  [1];  [1];  [2];  [3];  [3];  [1]
  1. Lawrence Livermore National Laboratory
  2. Sandia National Laboratories
  3. University of Oregon
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1778645
Report Number(s):
LLNL-CONF-809192
1015172
DOE Contract Number:  
AC52-07NA27344
Resource Type:
Conference
Resource Relation:
Conference: Atlanta, GA, United States
Country of Publication:
United States
Language:
English
Subject:
Computer science

Citation Formats

Wood, C, Georgakoudis, G, Beckingsale, D, Poliakoff, D, Huck, K, Malony, A D, and Gamblin, T. Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning. United States: N. p., 2020. Web.
Wood, C, Georgakoudis, G, Beckingsale, D, Poliakoff, D, Huck, K, Malony, A D, & Gamblin, T. Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning. United States.
Wood, C, Georgakoudis, G, Beckingsale, D, Poliakoff, D, Huck, K, Malony, A D, and Gamblin, T. 2020. "Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning". United States. https://www.osti.gov/servlets/purl/1778645.
@article{osti_1778645,
title = {Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning},
author = {Wood, C and Georgakoudis, G and Beckingsale, D and Poliakoff, D and Huck, K and Malony, A D and Gamblin, T},
abstractNote = {},
doi = {},
url = {https://www.osti.gov/biblio/1778645}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {4}
}

Conference:
Other availability
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