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Title: Program optimizations: The interplay between power, performance, and energy

Journal Article · · Parallel Computing
 [1];  [1];  [2];  [2]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
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Practical considerations for future supercomputer designs will impose limits on both instantaneous power consumption and total energy consumption. Working within these constraints while providing the maximum possible performance, application developers will need to optimize their code for speed alongside power and energy concerns. This paper analyzes the effectiveness of several code optimizations including loop fusion, data structure transformations, and global allocations. A per component measurement and analysis of different architectures is performed, enabling the examination of code optimizations on different compute subsystems. Using an explicit hydrodynamics proxy application from the U.S. Department of Energy, LULESH, we show how code optimizations impact different computational phases of the simulation. This provides insight for simulation developers into the best optimizations to use during particular simulation compute phases when optimizing code for future supercomputing platforms. Lastly, we examine and contrast both x86 and Blue Gene architectures with respect to these optimizations.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000; AC52-07NA27344
OSTI ID:
1263593
Alternate ID(s):
OSTI ID: 1333390; OSTI ID: 1397977
Report Number(s):
SAND-2016-4742J; LLNL-JRNL-679744; PII: S0167819116300369
Journal Information:
Parallel Computing, Journal Name: Parallel Computing; ISSN 0167-8191
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 6 works
Citation information provided by
Web of Science

References (1)

Application-level power and performance characterization and optimization on IBM Blue Gene/Q systems journal January 2013

Cited By (1)

Gradient Boosted Trees Predictive Models for Surface Roughness in High-Speed Milling in the Steel and Aluminum Metalworking Industry journal July 2019

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97 MATHEMATICS AND COMPUTING
optimization
power
energy
performance
97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE