Design and analysis of scheduling strategies for multi-CPU and multi-GPU architectures

Abstract : In this paper, we present a comparison of scheduling strategies for heterogeneous multi-CPU and multi-GPU architectures. We designed and evaluated four scheduling strategies on top of XKaapi runtime: work stealing, data-aware work stealing, locality-aware work stealing, and Heterogeneous Earliest-Finish-Time (HEFT). On a heterogeneous architecture with 12 CPUs and 8 GPUs, we analysed our scheduling strategies with four benchmarks: a BLAS-1 AXPY vector operation, a Jacobi 2D iterative computation, and two linear algebra algorithms Cholesky and LU. We conclude that the use of work stealing may be efficient if task annotations are given along with a data locality strategy. Furthermore, our experimental results suggests that HEFT scheduling performs better on applications with very regular computations and low data locality.
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Parallel Computing, Elsevier, 2015, 44, pp.37-52. 〈10.1016/j.parco.2015.03.001〉
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Contributeur : Joao Vicente Ferreira Lima <>
Soumis le : lundi 23 mars 2015 - 01:11:21
Dernière modification le : mercredi 11 avril 2018 - 01:55:44

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Joao Vicente Ferreira Lima, Thierry Gautier, Vincent Danjean, Bruno Raffin, Nicolas Maillard. Design and analysis of scheduling strategies for multi-CPU and multi-GPU architectures. Parallel Computing, Elsevier, 2015, 44, pp.37-52. 〈10.1016/j.parco.2015.03.001〉. 〈hal-01132037〉

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