Parsnip

Abstract

Data race detection is a useful dynamic analysis for multithreaded programs that is a key building block in record-and-replay, enforcing strong consistency models, and detecting concurrency bugs. Existing software race detectors are precise but slow, and hardware support for precise data race detection relies on assumptions like type safety that many programs violate in practice. We propose P, a fully precise hardware-supported data race detector. P exploits new insights into the redundancy of race detection metadata to reduce storage overheads. P also adopts new race detection metadata encodings that accelerate the common case while preserving soundness and completeness. When bounded hardware resources are exhausted, P falls back to a software race detector to preserve correctness. P does not assume that target programs are type safe, and is thus suitable for race detection on arbitrary code. Our evaluation of P on several PARSEC benchmarks shows that performance overheads range from negligible to 2.6x, with an average overhead of just 1.5x. Moreover, P outperforms the state-of-the-art Radish hardware race detector by 4.6x. CCS CONCEPTS • Computer systems organization → Multicore architectures; • Software and its engineering → Software maintenance tools;