Design and analysis of scheduling strategies for multi-CPU and multi-GPU architectures - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Parallel Computing Year : 2015

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.
Fichier principal
Vignette du fichier
parco2014.pdf (839.53 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01132037 , version 1 (23-03-2015)

Identifiers

Cite

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, 2015, 44, pp.37-52. ⟨10.1016/j.parco.2015.03.001⟩. ⟨hal-01132037⟩
201 View
513 Download

Altmetric

Share

Gmail Facebook X LinkedIn More