期刊
PROCEEDINGS OF THE IEEE
卷 106, 期 11, 页码 2068-2083出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPROC.2018.2841200
关键词
High-performance computing; performance tuning programming systems
资金
- Exascale Computing Project [17-SC-20-SC]
- U.S. Department of Energy Office of Science
- National Nuclear Security Administration
- National Science Foundation [ACI-1642441]
- U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
- U.S. Department of Energy, Office of Advanced Scientific Computing Research (ASCR), Scientific Discovery through Advanced Computing (SciDAC) program [ER26054]
- National Science Award [SHF-1564074]
- ASCR X-Stack Project [ER26143]
- Department of Defense through University of Maryland
Autotuning refers to the automatic generation of a search space of possible implementations of a computation that are evaluated through models and/or empirical measurement to identify the most desirable implementation. Autotuning has the potential to dramatically improve the performance portability of petascale and exascale applications. To date, autotuning has been used primarily in high-performance applications through tunable libraries or previously tuned application code that is integrated directly into the application. This paper draws on the authors' extensive experience applying autotuning to high-performance applications, describing both successes and future challenges. If autotuning is to be widely used in the HPC community, researchers must address the software engineering challenges, manage configuration overheads, and continue to demonstrate significant performance gains and portability across architectures. In particular, tools that configure the application must be integrated into the application build process so that tuning can be reapplied as the application and target architectures evolve.
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