Abstract Software and Hardware Support for Data-Race Exceptions

Some researchers have proposed data-race exceptions to mitigate the ill effects of data races in shared-memory multithreaded programs. Data-race exceptions make every data race an explicit fail-stop error at run-time. Implementing data-race exceptions naturally requires accurate dynamic data-race detection with low performance overhead, yet existing data-race detectors compromise either accuracy or performance. Hardware data-race detectors solutions are fast, but inaccurate. Accurate software data-race detectors slow execution by several times. This dissertation presents three new systems to bring accurate and fast language-level data-race exceptions closer to feasibility. Race Detection in Software and Hardware (RADISH) accelerates an accurate software data-race detection algorithm by mapping common cases to highly-optimized hardware support. By falling back to software support in rare cases, RADISH maintains full accuracy for low-level programs while achieving good performance. We show RADISH’s accuracy via its equivalence to a canonical accurate software algorithm for data-race detection. Low-level Abstractable Race Detection (LARD) virtualizes accurate low-level data-race detectors, such as RADISH, to support accurate data-race detection for high-level languages