Multi-slicing: a compiler-supported parallel approach to data dependence profiling

  • Authors:

  • Hongtao Yu;Zhiyuan Li

  • Affiliations:

  • Purdue University, USA;Purdue University, USA

  • Venue:
  • Proceedings of the 2012 International Symposium on Software Testing and Analysis
  • Year:
  • 2012

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Abstract

Retrofitting existing software for the increasingly dominant multicore microprocessors has a strong appeal from the economic point of view. One of the key issues in such an effort is to fully understand the data dependences in the existing software. Unfortunately, current compilers have quite limited ability to analyze data dependences. Therefore, execution-driven data dependence profiling has gained significant interest because it can resolve memory access ambiguity exactly during program execution, which allows data dependences to be analyzed exactly. Although such dependence profiling is valid for specific inputs only, the insight it provides can be highly valuable to software engineers in their parallelization effort. On the other hand, dependence profiling itself can take tremendous memory and machine time. In this paper, we propose a novel dependence profiling method which, with the support of several new compiler and runtime techniques, partitions the profiling task into many independent slices, each requiring significantly less memory. Different slices can be profiled in parallel, producing subgraphs which are eventually combined automatically into the complete data dependence graph by the compiler. The slices can be extracted with different degrees of granularity. Experiments show that, for several well-known benchmark programs, our parallel scheme shortens the profiling time by a few orders of magnitude.