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Area of Science:

  • Computer Science
  • Software Engineering
  • Parallel Computing

Background:

  • Multicore systems necessitate automatic parallelization, especially for legacy programs lacking source code.
  • Binary-level dependence analysis is crucial but challenging due to reduced code semantics.
  • Existing dynamic analysis methods often miss parallelization opportunities.

Purpose of the Study:

  • To develop a novel runtime data dependence detection method for binary code.
  • To enable accurate automatic parallelization of legacy programs.
  • To overcome limitations of traditional source-level and simple dynamic analysis.

Main Methods:

  • Implementing dynamic abstract interpretation for runtime analysis of program 'hot regions'.
  • Collecting program semantics during execution to perform static analysis dynamically.
  • Utilizing the Padrone dynamic code modification framework for implementation and evaluation.

Main Results:

  • Accurate data dependence detection at the binary level with low performance overhead.
  • Successful application on PolyBench kernels and SPEC CPU programs.
  • Demonstrated ability to resume analysis on successive hot region re-entries.

Conclusions:

  • Dynamic abstract interpretation is a viable and effective approach for binary-level data dependence analysis.
  • The method enhances automatic parallelization capabilities for legacy software.
  • No source code, recompilation, or hardware support is required.