HiRace: Accurate and Fast Data Race Checking for GPU Programs

Data races are egregious concurrency bugs that are especially problematic in performance-oriented GPU codes where large thread counts and multiple shared memory regions tend to exacerbate them. In this work, we present a new dynamic data-race checker called HiRace, whose key novelty is an innovative state machine designed to capitalize on the bulk-synchronous hierarchical GPU programming model. This state machine condenses an arbitrarily long access history into a constant-size state. We evaluate HiRace on a large, calibrated data-race benchmark suite. In over 3,500 studied executions of 580 CUDA kernels, 346 of which contain data races, we found HiRace to detect races missed by other tools without raising false alarms and to be more than 10 times faster on average than the current state of the art with half the memory overhead.