Improving fine-grained irregular shared-memory benchmarks by data reordering

  • Authors:

  • Y. Charlie Hu;Alan Cox;Willy Zwaenepoel

  • Affiliations:

  • Department of Computer Science, Rice University, Houston, Texas;Department of Computer Science, Rice University, Houston, Texas;Department of Computer Science, Rice University, Houston, Texas

  • Venue:
  • Proceedings of the 2000 ACM/IEEE conference on Supercomputing
  • Year:
  • 2000

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Abstract

We demonstrate that data reordering can substantially improve the performance of fine-grained irregular shared-memory benchmarks, on both hardware and software shared-memory systems. In particular, we evaluate two distinct data reordering techniques that seek to co-locate in memory objects in close proximity in the physical system modeled by the computation. The effects of these techniques are increased spatial locality and reduced false sharing. We evaluate the effectiveness of the data reordering techniques on a set of five irregular applications from SPLASH-2 and Chaos. We implement both techniques in a small library, allowing us to enable them in an application by adding less than 10 lines of code. Our results on one hardware and two software shared-memory systems show that, with data reordering during initialization, the performance of these applications is improved by 12 percent to 99 percent on the Origin 2000, 30 percent to 366 percent on TreadMarks, and 14 percent to 269 percent on HLRC.