Computation regrouping: restructuring programs for temporal data cache locality

  • Authors:

  • Venkata K. Pingali;Sally A. McKee;Wilson C. Hseih;John B. Carter

  • Affiliations:

  • Information Sciences Institute, University of Southern California;School of Computing, University of Utah;School of Computing, University of Utah;School of Computing, University of Utah

  • Venue:
  • ICS '02 Proceedings of the 16th international conference on Supercomputing
  • Year:
  • 2002

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Abstract

Data access costs contribute significantly to the execution time of applications with complex data structures. As the latency of memory accesses becomes high relative to processor cycle times, application performance is increasingly limited by memory performance. In some situations it may be reasonable to trade increased computation costs for reduced memory costs. The contributions of this paper are three-fold: we provide a detailed analysis of the memory performance of a set of seven, memory-intensive benchmarks; we describe Computation Regrouping, a general, source-level approach to improving the overall performance of these applications by improving temporal locality to reduce cache and TLB miss ratios (and thus memory stall times); and we demonstrate significant performance improvements from applying Computation Regrouping to our suite of seven benchmarks. With Computation Regrouping, we observe an average speedup of 1.97, with individual speedups ranging from 1.26 to 3.03. Most of this improvement comes from eliminating memory stall time.