2D-Profiling: Detecting Input-Dependent Branches with a Single Input Data Set

  • Authors:

  • Hyesoon Kim;M. Aater Suleman;Onur Mutlu;Yale N. Patt

  • Affiliations:

  • University of Texas at Austin;University of Texas at Austin;University of Texas at Austin;University of Texas at Austin

  • Venue:
  • Proceedings of the International Symposium on Code Generation and Optimization
  • Year:
  • 2006

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Abstract

Static compilers use profiling to predict run-time program behavior. Generally, this requires multiple input sets to capture wide variations in run-time behavior. This is expensive in terms of resources and compilation time. We introduce a new mechanism, 2D-profiling, which profiles with only one input set and predicts whether the result of the profile would change significantly across multiple input sets. We use 2D-profiling to predict whether a branch's prediction accuracy varies across input sets. The key insight is that if the prediction accuracy of an individual branch varies significantly over a profiling run with one input set, then it is more likely that the prediction accuracy of that branch varies across input sets. We evaluate 2D-profiling with the SPEC CPU 2000 integer benchmarks and show that it can identify input-dependent branches accurately.