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Book Sections Year : 2018

Towards a Unified CPU–GPU code hybridization: A GPU Based Optimization Strategy Efficient on Other Modern Architectures

Abstract

In this paper, we suggest a different methodology to shorten the code optimization development time while getting a unified code with good performance on different targeted devices. In the scope of this study, experiments are illustrated on a Discontinuous Galerkin code applied to Computational Fluid Dynamics. Tests are performed on CPUs, KNL Xeon-Phi and GPUs where performance comparison confirms that the GPU optimization guideline leads to efficient versions on CPU and Xeon-Phi for this kind of scientific applications. Based on these results, we finally suggest a methodology to end-up with an efficient hybridized CPU–GPU implementation.
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Dates and versions

hal-01742774 , version 1 (26-03-2018)

Identifiers

  • HAL Id : hal-01742774 , version 1

Cite

Ludomir Oteski, Guillaume Colin de Verdière, Sylvain Contassot-Vivier, Stephane Vialle, Juliet Ryan. Towards a Unified CPU–GPU code hybridization: A GPU Based Optimization Strategy Efficient on Other Modern Architectures. Parallel Computing is Everywhere,, 2018. ⟨hal-01742774⟩
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