Design and analysis of scheduling strategies for multi-CPU and multi-GPU architecturesReport as inadecuate

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1 MOAIS - PrograMming and scheduling design fOr Applications in Interactive Simulation Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d-Informatique de Grenoble 2 UFRGS - Instituto de Informática da UFRGS

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.

Keywords : Work stealing Data-flow dependencies Task parallelism Accelerators Parallel programming

Author: Joao Vicente Ferreira Lima - Thierry Gautier - Vincent Danjean - Bruno Raffin - Nicolas Maillard -



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