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Data-centric combinatorial optimization of parallel code

Published:27 February 2016Publication History
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Abstract

Memory performance is one essential factor for tapping into the full potential of the massive parallelism of GPU. It has motivated some recent efforts in GPU cache modeling. This paper presents a new data-centric way to model the performance of a system with heterogeneous memory resources. The new model is composable, meaning it can predict the performance difference due to placing data differently by profiling the execution just once.

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  1. Data-centric combinatorial optimization of parallel code

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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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