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Title: Statistical and machine learning models for optimizing energy in parallel applications

Abstract

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. Here, we demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [2];  [2];  [3]
  1. The Univ. of Queensland, Brisbane (Australia)
  2. Cray Inc., Bloomington, MN (United States)
  3. 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 National Nuclear Security Administration (NNSA)
OSTI Identifier:
1573175
Report Number(s):
LLNL-JRNL-769611
Journal ID: ISSN 1094-3420; 960894
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
International Journal of High Performance Computing Applications
Additional Journal Information:
Journal Volume: 33; Journal Issue: 6; Journal ID: ISSN 1094-3420
Publisher:
SAGE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; energy efficiency; performance; regression modeling; machine learning; high performance computing

Citation Formats

Endrei, Mark, Jin, Chao, Dinh, Minh Ngoc, Abramson, David, Poxon, Heidi, DeRose, Luiz, and de Supinski, Bronis R. Statistical and machine learning models for optimizing energy in parallel applications. United States: N. p., 2019. Web. doi:10.1177/1094342019842915.
Endrei, Mark, Jin, Chao, Dinh, Minh Ngoc, Abramson, David, Poxon, Heidi, DeRose, Luiz, & de Supinski, Bronis R. Statistical and machine learning models for optimizing energy in parallel applications. United States. https://doi.org/10.1177/1094342019842915
Endrei, Mark, Jin, Chao, Dinh, Minh Ngoc, Abramson, David, Poxon, Heidi, DeRose, Luiz, and de Supinski, Bronis R. Thu . "Statistical and machine learning models for optimizing energy in parallel applications". United States. https://doi.org/10.1177/1094342019842915. https://www.osti.gov/servlets/purl/1573175.
@article{osti_1573175,
title = {Statistical and machine learning models for optimizing energy in parallel applications},
author = {Endrei, Mark and Jin, Chao and Dinh, Minh Ngoc and Abramson, David and Poxon, Heidi and DeRose, Luiz and de Supinski, Bronis R.},
abstractNote = {Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. Here, we demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.},
doi = {10.1177/1094342019842915},
url = {https://www.osti.gov/biblio/1573175}, journal = {International Journal of High Performance Computing Applications},
issn = {1094-3420},
number = 6,
volume = 33,
place = {United States},
year = {2019},
month = {4}
}

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