A compiler framework for speculative optimizations

  • Authors:

  • Jin Lin;Tong Chen;Wei-Chung Hsu;Pen-Chung Yew;Roy Dz-Ching Ju;Tin-Fook Ngai;Sun Chan

  • Affiliations:

  • University of Minnesota, Minneapolis, MN;University of Minnesota, Minneapolis, MN;University of Minnesota, Minneapolis, MN;University of Minnesota, Minneapolis, MN;Intel Microprocessor Research Lab, Santa Clara, CA;Intel Microprocessor Research Lab, Santa Clara, CA;Intel Microprocessor Research Lab, Santa Clara, CA

  • Venue:
  • ACM Transactions on Architecture and Code Optimization (TACO)
  • Year:
  • 2004

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Abstract

Speculative execution, such as control speculation or data speculation, is an effective way to improve program performance. Using edge/path profile information or simple heuristic rules, existing compiler frameworks can adequately incorporate and exploit control speculation. However, very little has been done so far to allow existing compiler frameworks to incorporate and exploit data speculation effectively in various program transformations beyond instruction scheduling. This paper proposes a speculative static single assignment form to incorporate information from alias profiling and/or heuristic rules for data speculation, thus allowing existing frameworks to be extended to support both control and data speculation. Such a general framework is very useful for EPIC architectures that provide run-time checking (such as advanced load address table) on data speculation to guarantee the correctness of program execution. We use SSAPRE as one example to illustrate how to incorporate data speculation in partial redundancy elimination, register promotion, and strength reduction. Our extended framework allows both control and data speculations to be performed on top of SSAPRE and, thus, enables more aggressive speculative optimizations. The proposed framework has been implemented on Intel's Open Research Compiler. We present experimental data on some SPEC2000 benchmark programs to demonstrate the usefulness of this framework.