PARSNIP: Performant Architecture for Race Safety with No Impact on Precision

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 PARSNIP, a fully precise hardware-supported data race detector. PARSNIP exploits new insights into the redundancy of race detection metadata to reduce storage overheads. PARSNIP also adopts new race detection metadata encodings that accelerate the common case while preserving soundness and completeness. When bounded hardware resources are exhausted, PARSNIP falls back to a software race detector to preserve correctness. PARSNIP does not assume that target programs are type safe, and is thus suitable for race detection on arbitrary code. Our evaluation of PARSNIP on several PARSEC benchmarks shows that performance overheads range from negligible to 2.6x, with an average overhead of just 1.5x. Moreover, Parsnip outperforms the state-of-the-art Radish hardware race detector by 4.6x

Efficient Processor Support for DRFx, a Memory Model with Exceptions

A longstanding challenge of shared-memory concurrency is to provide a memory model that allows for efficient implementation while providing strong and simple guarantees to programmers. The C++0x and Java memory models admit a wide variety of compiler and hardware optimizations and provide sequentially consistent (SC) semantics for data-race-free programs. However, they either do not provide any semantics (C++0x) or provide a hard-tounderstand semantics (Java) for racy programs, compromising the safety and debuggability of such programs. In earlier work we proposed the DRFx memory model, which addresses this problem by dynamically detecting potential violations of SC due to the interaction of compiler or hardware optimizations with data races and halting execution upon detection. In this paper, we present a detailed micro-architecture design for supporting the DRFx memory model, formalize the design and prove its correctness, and evaluate the design using a hardware simulator. We describe a set of DRFx-compliant complexity-effective optimizations which allow us to attain performance close to that of TSO (Total Store Model) and DRF0 while providing strong guarantees for all programs