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

Thierry Gautier, Joao V. F. Lima, Nicolas Maillard, Bruno Raffin
Federal University of Rio Grande do Sul (UFRGS), Brazil
hal-00780890, 24 January 2013




   title={Locality-Aware Work Stealing on Multi-CPU and Multi-GPU Architectures},

   author={Gautier, Thierry and Ferreira Lima, Joao Vicente and Maillard, Nicolas and Raffin, Bruno},


   affiliation={MOAIS – INRIA Grenoble Rh{^o}ne-Alpes / LIG Laboratoire d’Informatique de Grenoble , Instituto de Inform{‘a}tica da UFRGS – UFRGS},

   booktitle={6th Workshop on Programmability Issues for Heterogeneous Multicores (MULTIPROG)},

   address={Berlin, Allemagne},






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Most recent HPC platforms have heterogeneous nodes composed 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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