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Graph partitioning applied to DAG scheduling to reduce NUMA effects

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Published:10 February 2018Publication History
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Abstract

The complexity of shared memory systems is becoming more relevant as the number of memory domains increases, with different access latencies and bandwidth rates depending on the proximity between the cores and the devices containing the data. In this context, techniques to manage and mitigate non-uniform memory access (NUMA) effects consist in migrating threads, memory pages or both and are typically applied by the system software.

We propose techniques at the runtime system level to reduce NUMA effects on parallel applications. We leverage runtime system metadata in terms of a task dependency graph. Our approach, based on graph partitioning methods, is able to provide parallel performance improvements of 1.12X on average with respect to the state-of-the-art.

References

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      • Published in

        cover image ACM SIGPLAN Notices
        ACM SIGPLAN Notices  Volume 53, Issue 1
        PPoPP '18
        January 2018
        426 pages
        ISSN:0362-1340
        EISSN:1558-1160
        DOI:10.1145/3200691
        Issue’s Table of Contents
        • cover image ACM Conferences
          PPoPP '18: Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
          February 2018
          442 pages
          ISBN:9781450349826
          DOI:10.1145/3178487

        Copyright © 2018 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 10 February 2018

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