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Layer-Based Scheduling of Parallel Tasks for Heterogeneous Cluster Platforms

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Algorithms and Architectures for Parallel Processing (ICA3PP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8285))

Abstract

The programming model of parallel tasks is a suitable programming abstraction for parallel applications running on heterogeneous clusters, which are clusters composed of multiple subclusters. In this model, an application is decomposed into parallel tasks, each of which can be executed on an arbitrary number of processors. The advantage of this programming approach is that each task only needs to be implemented for a homogeneous environment while the complete application can still benefit from the entire performance of the heterogeneous cluster by a concurrent execution of independent parallel tasks on different subclusters. The execution of such an application on a specific platform is controlled by a schedule that maps each parallel task onto a set of processors.

In this article, we propose an algorithm for the scheduling of parallel tasks with precedence constraints on heterogeneous clusters. This algorithm is an extension of a layer-based scheduling approach for homogeneous platforms with an additional phase that assigns the parallel tasks to appropriate subclusters. Three different versions of this additional phase are considered. An experimental evaluation, based on simulation results as well as on measurements with different application benchmarks, shows that the proposed scheduling approach outperforms existing scheduling algorithms in most situations.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Technische Universität Chemnitz, 09107, Chemnitz, Germany

    Jörg Dümmler & Gudula Rünger

Authors
  1. Jörg Dümmler
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  2. Gudula Rünger
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Editor information

Editors and Affiliations

  1. Institute of Computer Science, Cracow University of Technology, Warszawska 24, 31-155, Cracow, Poland

    Joanna Kołodziej

  2. Dipartimento di Ingegneria, Seconda Universita’ di Napoli, 81031, Aversa, CE, Italy

    Beniamino Di Martino

  3. DIMES and ICAR-CNR, c/o Università della Calabria, 87036, Rende, CS, Italy

    Domenico Talia

  4. College of Computing and Information Sciences, Rochester Institute of Technology, 14623, Rochester, NY, USA

    Kaiqi Xiong

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Dümmler, J., Rünger, G. (2013). Layer-Based Scheduling of Parallel Tasks for Heterogeneous Cluster Platforms. In: Kołodziej, J., Di Martino, B., Talia, D., Xiong, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2013. Lecture Notes in Computer Science, vol 8285. Springer, Cham. https://doi.org/10.1007/978-3-319-03859-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-03859-9_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03858-2

  • Online ISBN: 978-3-319-03859-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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