Locality-Aware Work Stealing on Multi-CPU and Multi-GPU Architectures

Abstract : Most recent HPC platforms have heterogeneous nodes com- posed of a combination of multi-core CPUs and accelerators, like GPUs. Scheduling on such architectures relies on a static partitioning and cost model. In this paper, we present a locality-aware work stealing scheduler for multi-CPU and multi-GPU architectures, which relies on the XKaapi runtime system. We show performance results on two dense linear algebra kernels, Cholesky (POTRF) and LU (GETRF) factorization, to evaluate our scheduler on a heterogeneous architecture composed of two hexa-core CPUs and eight NVIDIA Fermi GPUs. Our experiments show that an online locality-aware scheduling achieve performance results as good as static strategies, and in most cases outperform them.
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Communication dans un congrès
6th Workshop on Programmability Issues for Heterogeneous Multicores (MULTIPROG), Jan 2013, Berlin, Germany. 2013
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Thierry Gautier, Joao Vicente Ferreira Lima, Nicolas Maillard, Bruno Raffin. Locality-Aware Work Stealing on Multi-CPU and Multi-GPU Architectures. 6th Workshop on Programmability Issues for Heterogeneous Multicores (MULTIPROG), Jan 2013, Berlin, Germany. 2013. 〈hal-00780890〉

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