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Affinity-aware work-stealing for integrated CPU-GPU processors

Published:27 February 2016Publication History
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Abstract

Recent integrated CPU-GPU processors like Intel's Broadwell and AMD's Kaveri support hardware CPU-GPU shared virtual memory, atomic operations, and memory coherency. This enables fine-grained CPU-GPU work-stealing, but architectural differences between the CPU and GPU hurt the performance of traditionally-implemented work-stealing on such processors. These architectural differences include different clock frequencies, atomic operation costs, and cache and shared memory latencies. This paper describes a preliminary implementation of our work-stealing scheduler, Libra, which includes techniques to deal with these architectural differences in integrated CPU-GPU processors. Libra's affinity-aware techniques achieve significant performance gains over classically-implemented work-stealing. We show preliminary results using a diverse set of nine regular and irregular workloads running on an Intel Broadwell Core-M processor. Libra currently achieves up to a 2× performance improvement over classical work-stealing, with a 20% average improvement.

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  • Published in

    cover image ACM SIGPLAN Notices
    ACM SIGPLAN Notices  Volume 51, Issue 8
    PPoPP '16
    August 2016
    405 pages
    ISSN:0362-1340
    EISSN:1558-1160
    DOI:10.1145/3016078
    Issue’s Table of Contents
    • cover image ACM Conferences
      PPoPP '16: Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
      February 2016
      420 pages
      ISBN:9781450340922
      DOI:10.1145/2851141

    Copyright © 2016 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 27 February 2016

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