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Scalable Loop Self-Scheduling Schemes for Large-Scale Clusters and Cloud Systems

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Abstract

Cloud systems have demonstrated the powerful computation and storage capability in many scientific applications. In this paper, we propose a class of scalable distributed loop self-scheduling schemes to achieve good load balancing and scalability. We implemented these schemes on a large-scale cluster and on a heterogeneous cloud system. The schemes consider the distribution of the output data, which can help reduce communication overhead and improve scalability. We evaluated the schemes using four scientific computations: Mandelbrot set, adjoint convolution, matrix multiplication and quick sort. The results show that the new schemes achieve better load balancing, better scalability and better overall performance than standard distributed loop self-scheduling schemes.

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Acknowledgments

The authors acknowledge: (1) support by NSF grant (HRD-0932339) to the University of Texas at San Antonio; and (2) time grants to access the facilities of Institute for Cyber Security(ICS) of University of Texas at San Antonio and FutureGrid at Indiana University, Bloomington; (3) the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC, visualization, database, or grid resources that have contributed to the research results reported within this paper. URL:http://www.tacc.utexas.edu.

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  1. Department of Computer Science, University of Texas at San Antonio, San Antonio, TX, USA

    Yiming Han & Anthony T. Chronopoulos

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  1. Yiming Han
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  2. Anthony T. Chronopoulos
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Correspondence to Anthony T. Chronopoulos.

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Han, Y., Chronopoulos, A.T. Scalable Loop Self-Scheduling Schemes for Large-Scale Clusters and Cloud Systems. Int J Parallel Prog 45, 595–611 (2017). https://doi.org/10.1007/s10766-016-0434-5

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