Accelerating Data-Dependence Profiling with Static Hints

Abstract

Data-dependence profiling is a program-analysis technique to discover potential parallelism in sequential programs. Contrary to purely static dependence analysis, profiling has the advantage that it captures only those dependences that actually occur during execution. Lacking critical runtime information such as the value of pointers and array indices, purely static analysis may overestimate the amount of dependences. On the downside, dependence profiling significantly slows down the program, not seldom prolonging execution by a factor of 100. In this paper, we propose a hybrid approach that substantially reduces this overhead. First, we statically identify persistent data dependences that will appear in any execution. We then exclude the affected source-code locations from instrumentation, allowing the profiler to skip them at runtime and avoiding the associated overhead. At the end, we merge static and dynamic dependences. We evaluated our approach with 38 benchmarks from two benchmark suites and obtained a median reduction of the profiling time by 62% across all the benchmarks.