Volume 63, 2018CEMRACS 2016 - Numerical challenges in parallel scientific computing
|Page(s)||152 - 178|
|Published online||19 October 2018|
Building and Auto-Tuning Computing Kernels: Experimenting with Boast and Starpu in the Gysela Code★
Maison de la Simulation, CEA, CNRS, Univ. Paris-Sud, UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
2 CEA, IRFM, F-13108 Saint-Paul-lez-Durance
3 Laboratoire d’Informatique de Grenoble/Université Grenoble Alpes
4 Univ. Lyon, Inria, CNRS, ENS de Lyon, Univ. Claude-Bernard Lyon 1, LIP
5 Laboratoire d’Informatique de Grenoble/CNRS
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.
© EDP Sciences, SMAI 2018
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