This paper presents Fast Instrumentation Bias (FIB), a sound and complete dynamic data race detection algorithm that improves performance by reducing or eliminating the costs of analysis atomicity. In addition to checking for errors in target programs, dynamic data race detectors must introduce synchronization to guard against metadata races that may corrupt analysis state and compromise soundness or completeness. Pessimistic analysis synchronization can account for nontrivial performance overhead in a data race detector.
The core contribution of FIB is a novel cooperative ownership-based synchronization protocol whose states and transitions are derived purely from preexisting analysis metadata and logic in a standard data race detection algorithm. By exploiting work already done by the analysis, FIB ensures atomicity of dynamic analysis actions with zero additional time or space cost in the common case. Analysis of temporally thread-local or read-shared accesses completes safely with no synchronization. Uncommon write-sharing transitions require synchronous cross-thread coordination to ensure common cases may proceed synchronization-free.
We implemented FIB in the Jikes RVM Java virtual machine. Experimental evaluation shows that FIB eliminates nearly all instrumentation atomicity costs on programs where data often experience windows of thread-local access. Adaptive extensions to the ownership policy effectively eliminate high coordination costs of the core ownership protocol on programs with high rates of serialized sharing. FIB outperforms a naive pessimistic synchronization scheme by 50% on average. Compared to a tuned optimistic metadata synchronization scheme based on conventional fine-grained atomic compare-and-swap operations, FIB is competitive overall, and up to 17% faster on some programs. Overall, FIB effectively exploits latent analysis and program invariants to bring strong integrity guarantees to an otherwise unsynchronized data race detection algorithm at minimal cost.
Wood, Benjamin P.; Cao, Man; Bond, Michael D.; and Grossman, Dan, "Instrumentation bias for dynamic data race detection". Proceedings of the ACM on Programming Languages, v. 1, Issue OOPSLA, Article No. 69, October 2017. https://doi.org/10.1145/3133893