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- W2898099374 abstract "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." @default.
- W2898099374 created "2018-11-02" @default.
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- W2898099374 date "2018-11-01" @default.
- W2898099374 modified "2023-10-14" @default.
- W2898099374 title "Autotuning in High-Performance Computing Applications" @default.
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- W2898099374 doi "https://doi.org/10.1109/jproc.2018.2841200" @default.
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