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JAWS: a JavaScript framework for adaptive CPU-GPU work sharing

Published:24 January 2015Publication History
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Abstract

This paper introduces jAWS, a JavaScript framework for adaptive work sharing between CPU and GPU for data-parallel workloads. Unlike conventional heterogeneous parallel programming environments for JavaScript, which use only one compute device when executing a single kernel, jAWS accelerates kernel execution by exploiting both devices to realize full performance potential of heterogeneous multicores. jAWS employs an efficient work partitioning algorithm that finds an optimal work distribution between the two devices without requiring offline profiling. The jAWS runtime provides shared arrays for multiple parallel contexts, hence eliminating extra copy overhead for input and output data. Our preliminary evaluation with both CPU-friendly and GPU-friendly benchmarks demonstrates that jAWS provides good load balancing and efficient data communication between parallel contexts, to significantly outperform best single-device execution.

References

  1. WebCL Standard. URL http://www.khronos.org/webcl/.Google ScholarGoogle Scholar
  2. Web Worker. URL http://www.w3.org/TR/workers.Google ScholarGoogle Scholar
  3. M. Boyer, K. Skadron, S. Che, and N. Jayasena. Load Balancing in a Changing World: Dealing with Heterogeneity and Performance Variability. In CF, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Grauer-Gray, L. Xu, R. Searles, S. Ayalasomayajula, and J. Cavazos. Auto-tuning a high-level language targeted to GPU codes. In Proceedings of Innovative Parallel Computing (InPar), 2012.Google ScholarGoogle ScholarCross RefCross Ref
  5. P. Pandit and R. Govindarajan. Fluidic Kernels: Cooperative Execution of OpenCL Programs on Multiple Heterogeneous Devices. In CGO, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM SIGPLAN Notices
        ACM SIGPLAN Notices  Volume 50, Issue 8
        PPoPP '15
        August 2015
        290 pages
        ISSN:0362-1340
        EISSN:1558-1160
        DOI:10.1145/2858788
        • Editor:
        • Andy Gill
        Issue’s Table of Contents
        • cover image ACM Conferences
          PPoPP 2015: Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
          January 2015
          290 pages
          ISBN:9781450332057
          DOI:10.1145/2688500

        Copyright © 2015 Owner/Author

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 24 January 2015

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