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Building and Auto-Tuning Computing Kernels: Experimenting with BOAST and StarPU in the GYSELA Code

Abstract : Modeling turbulent transport is a major goal in order to predict confinement performance in a tokamak plasma. The gyrokinetic framework considers a computational domain in five dimensions to look at kinetic issues in a plasma; this leads to huge computational needs. Therefore, optimization of the code is an especially important aspect, especially since coprocessors and complex manycore architectures are foreseen as building blocks for Exascale systems. This project aims to evaluate the applicability of two auto-tuning approaches with the BOAST and StarPU tools on the gysela code in order to circumvent performance portability issues. A specific computation intensive kernel is considered in order to evaluate the benefit of these methods. StarPU enables to match the performance and even sometimes outperform the hand-optimized version of the code while leaving scheduling choices to an automated process. BOAST on the other hand reveals to be well suited to get a gain in terms of execution time on four architectures. Speedups in-between 1.9 and 5.7 are obtained on a cornerstone computation intensive kernel.
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Contributor : Jean-Francois Méhaut Connect in order to contact the contributor
Submitted on : Wednesday, October 31, 2018 - 9:05:54 AM
Last modification on : Thursday, October 7, 2021 - 3:12:43 AM
Long-term archiving on: : Friday, February 1, 2019 - 12:55:28 PM


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Julien Bigot, Virginie Grandgirard, Guillaume Latu, Jean-François Méhaut, Luís Felipe Millani, et al.. Building and Auto-Tuning Computing Kernels: Experimenting with BOAST and StarPU in the GYSELA Code. ESAIM: Proceedings, EDP Sciences, 2018, CEMRACS 2016 - Numerical challenges in parallel scientific computing, 63 (2018), pp.152 - 178. ⟨10.1051/proc/201863152⟩. ⟨hal-01909325⟩



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