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POSTER: A GPU-Friendly Skiplist Algorithm

Published:26 January 2017Publication History
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Abstract

We propose a design for a fine-grained lock-based skiplist optimized for Graphics Processing Units (GPUs). While GPUs are often used to accelerate streaming parallel computations, it remains a significant challenge to efficiently offload concurrent computations with more complicated data-irregular access and fine-grained synchronization. Natural building blocks for such computations would be concurrent data structures, such as skiplists, which are widely used in general purpose computations. Our design utilizes array-based nodes which are accessed and updated by warp-cooperative functions, thus taking advantage of the fact that GPUs are most efficient when memory accesses are coalesced and execution divergence is minimized. The proposed design has been implemented, and measurements demonstrate improved performance of up to 2.6x over skiplist designs for the GPU existing today.

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      • Published in

        cover image ACM SIGPLAN Notices
        ACM SIGPLAN Notices  Volume 52, Issue 8
        PPoPP '17
        August 2017
        442 pages
        ISSN:0362-1340
        EISSN:1558-1160
        DOI:10.1145/3155284
        Issue’s Table of Contents
        • cover image ACM Conferences
          PPoPP '17: Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
          January 2017
          476 pages
          ISBN:9781450344937
          DOI:10.1145/3018743

        Copyright © 2017 Owner/Author

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 26 January 2017

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