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International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems

PMBS 2013: High Performance Computing Systems. Performance Modeling, Benchmarking and Simulation pp 73–87Cite as

Analysis of Data Reuse in Task-Parallel Runtimes

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8551))

Abstract

This paper proposes a methodology to study the data reuse quality of task-parallel runtimes. We introduce an coarse-grain version of the reuse distance method called Kernel Reuse Distance (KRD). The metric is a low-overhead alternative designed to analyze data reuse at the socket level while minimizing perturbation to the parallel schedule. Using the KRD metric we show that reuse depends considerably on the system configuration (sockets, cores) and on the runtime scheduler. Furthermore, we correlate KRD with hardware metrics such as cache misses and work time inflation. Overall we found that KRD can be used effectively to assess data reuse in parallel applications. The study also revealed that several current runtimes suffer from severe bottlenecks at scale which often dominate performance.

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Acknowledgments

This work has been supported by a JSPS postdoctoral fellowship (P-12044). We would like to thank the anonymous reviewers for their valuable feedback.

Author information

Authors and Affiliations

  1. Global Scientific Information and Computing Center, Tokyo Institute of Technology, Tokyo, Japan

    Miquel Pericàs & Satoshi Matsuoka

  2. Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, Tokyo, Japan

    Abdelhalim Amer & Satoshi Matsuoka

  3. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan

    Kenjiro Taura

Authors
  1. Miquel Pericàs
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  2. Abdelhalim Amer
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  3. Kenjiro Taura
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  4. Satoshi Matsuoka
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Corresponding author

Correspondence to Miquel Pericàs .

Editor information

Editors and Affiliations

  1. University of Warwick Coventry, West Midlands, United Kingdom

    Stephen A. Jarvis

  2. University of Warwick Coventry, West Midlands, United Kingdom

    Steven A. Wright

  3. Sandia National Laboratories CSRI, Albuquerque, New Mexico, USA

    Simon D. Hammond

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© 2014 Springer International Publishing Switzerland

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Pericàs, M., Amer, A., Taura, K., Matsuoka, S. (2014). Analysis of Data Reuse in Task-Parallel Runtimes. In: Jarvis, S., Wright, S., Hammond, S. (eds) High Performance Computing Systems. Performance Modeling, Benchmarking and Simulation. PMBS 2013. Lecture Notes in Computer Science(), vol 8551. Springer, Cham. https://doi.org/10.1007/978-3-319-10214-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-10214-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10213-9

  • Online ISBN: 978-3-319-10214-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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