ParaShares: Finding the Important Basic Blocks in Multithreaded Programs

Understanding and optimizing multithreaded execution is a significant challenge. Numerous research and industrial tools debug parallel performance by combing through program source or thread traces for pathologies including communication overheads, data dependencies, and load imbalances. This work takes a new approach: it ignores any underlying pathologies, and focuses instead on pinpointing the exact locations in source code that consume the largest share of execution. Our new metric, ParaShares, scores and ranks all basic blocks in a program based on their share of parallel execution. For the eight benchmarks examined in this paper, ParaShare rankings point to just a few important blocks per application. The paper demonstrates two uses of this information, exploring how the important blocks vary across thread counts and input sizes, and making modest source code changes (fewer than 10 lines of code) that result in 14-92% savings in parallel program runtime.