Optimization of data prefetch helper threads with path-expression based statistical modeling

  • Authors:

  • Tor M. Aamodt;Paul Chow

  • Affiliations:

  • University of British Columbia, British Columbia, Canada;University of Toronto, Toronto, Ontario, Canada

  • Venue:
  • Proceedings of the 21st annual international conference on Supercomputing
  • Year:
  • 2007

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Abstract

This paper investigates helper threads that improve performance by prefetching data on behalf of an application's main thread. The focus is data prefetch helper threads that lack branch instructions and which generate prefetches for one dynamic instance of a delinquent load instruction per spawned helper thread. This form of helper thread, some-times called a simple p-thread, has been studied previously by Roth et al. [29, 26] who proposed a framework for optimizing their impact. A key step in that framework is predicting the performance impact of a helper thread. In this paper we propose and evaluate a novel performance prediction technique that achieves comparable results yet requires less detailed information about dynamic program behavior. This technique extends a path expression based statistical modeling framework [2] by incorporating information about branch correlation (which we show is important) and by considering data flow information in a statistical manner. Significantly, the profile information we use is similar to that provided within current optimizing compilers. This paper also provides the first comprehensive assessment of the sources of modeling error relevant to predicting the performance impact of simple p-threads.