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Staccato: Cache-Aware Work-Stealing Task Scheduler for Shared-Memory Systems

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

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Abstract

Parallel tasks work-stealing schedulers yield near-optimal tasks distribution (i.e. all CPU cores are loaded equally) and have low time, memory and inter-thread synchronizations. The key idea of work-stealing strategy is that when scheduler worker runs out of tasks for execution, it start stealing tasks from the queues of other workers. It’s been shown that double ended queues based on circular arrays are effective in this scenario. They are designed with an assumption that tasks pointer are stored in these data structures, while tasks object reside in heap memory. By modifying tasks queues so that they can hold task objects instead pointers we managed to increase the performance above 2.5 times on CPU bound applications and decrease last-level cache misses 30% compared to Intel TBB and Intel/MIT Cilk work-stealing schedulers.

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Acknowledgements

Research has been supported by the RFBR grants No. 18-01-00125-a and 16-07-01111.

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Authors and Affiliations

  1. Saint Petersburg State University, 7/9 Universitetskaya nab., St. Petersburg, 199034, Russia

    Ruslan Kuchumov & Vladimir Korkhov

  2. Institute of Applied Mathematical Research, Karelian Research Centre RAS, Petrozavodsk, Russia

    Andrey Sokolov

Authors
  1. Ruslan Kuchumov
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  2. Andrey Sokolov
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  3. Vladimir Korkhov
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Corresponding author

Correspondence to Vladimir Korkhov .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

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Kuchumov, R., Sokolov, A., Korkhov, V. (2018). Staccato: Cache-Aware Work-Stealing Task Scheduler for Shared-Memory Systems. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10963. Springer, Cham. https://doi.org/10.1007/978-3-319-95171-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-95171-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95170-6

  • Online ISBN: 978-3-319-95171-3

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