Fast loop-level data dependence profiling

Execution-driven data dependence profiling has gained significant interest as a tool to compensate the weakness of static data dependence analysis. Although such dependence profiling is valid for specific inputs only, its result can be used in many ways for program parallelization. Unfortunately, traditional hash-based dependence profiling can take tremendous memory and machine time, which severely limits its practical use. In this paper, we propose new compiler-based techniques to perform fast loop-level data dependence profiling. Firstly, using type consistency and alias information, our compiler embeds memory tags into the data structures in the original program such that memory addresses can be efficiently compared for dependence testing. This approach avoids the bytewise hashing overhead in conventional profiling methods. Secondly, we prove that a partial dependence graph obtained from profiling is sufficient for loop-level reordering transformations and parallelization. Such partial dependence graph can be obtained very fast, without having to exhaustively enumerate all dependence edges. Thirdly, our compiler partitions the profiling task into independent slices. Such slices can be profiled in parallel, producing subgraphs which are eventually combined automatically into the complete data dependence graph by the compiler. Experiments show that these techniques significantly reduce the memory use and shorten the profiling time (by an order of magnitude for several SPEC2006 benchmarks). Benchmarks too big to profile at all loop levels by previous methods can now be profiled fully within several hours.