Scientific applications vs. SPEC-FP: a comparison of program behavior

  • Authors:
  • Kyle Rupnow;Arun Rodrigues;Keith Underwood;Katherine Compton

  • Affiliations:
  • Univ. of Wisconsin, Madison, WI and Sandia National Labs, Albuquerque, NM;Univ. of Notre Dame, Notre Dame, IN;Sandia National Labs, Albuquerque, NM;Univ. of Wisconsin, Madison, WI

  • Venue:
  • Proceedings of the 20th annual international conference on Supercomputing
  • Year:
  • 2006

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Abstract

Many modern scientific applications execute on massively parallel collections of microprocessors. Supercomputers such as the Cray XT3 (Red Storm) and Blue Gene/L support thousands to tens of thousands of processors per parallel job. However, individual microprocessor performance remains a critical component of overall performance. Traditional approaches to improve scientific application performance concentrate on floating-point (FP) instructions; however, our studies show that in the scientific applications used at Sandia National Labs, integer instructions constitute a large and critical part of the instruction mix. Although the SPEC-FP benchmark suite is considered representative of FP workloads, it has a much smaller proportion of integer computation instructions than the Sandia scientific applications, with 22.9% as compared to 36.9%. Integer instructions in Sandia applications also behave differently than in SPEC-FP. Integer instruction outputs are reused 8.8x to 13.1x more often in SPEC-FP benchmarks, and integer dataflow in Sandia applications is more complex than in the SPEC-FP suite. In this work, we examine common dataflow and usage patterns of integer instructions---information essential to develop hardware techniques to accelerate critical scientific applications. We present statistics for SPEC-FP and Sandia applications, summarizing integer computation usage and the size, shape and interface (number of inputs/outputs) of dataflow graphs.