Program optimizations: The interplay between power, performance, and energy
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
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
Web of Science
Application-level power and performance characterization and optimization on IBM Blue Gene/Q systems
|
journal | January 2013 |
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