In a precise data race detector, a race is detected only if the execution exhibits a real race. In such tools, every memory access from each thread is typically checked by a happens-before algorithm. What’s the optimal runtime performance of such tools? In this paper, we identify that a significant percentage of memory access checks in real-world program executions are often redundant: removing these checks affects neither the precision nor the capability of race detection. We show that if all such redundant checks were eliminated with no cost, the optimal performance of a state-of-the-art dynamic race detector, FastTrack, could be improved by 90%, reducing its runtime overhead from 68X to 7X on a collection of CPU intensive benchmarks. We further develop a purely dynamic technique, ReX, that efficiently filters out redundant checks and apply it to FastTrack. With ReX, the runtime performance of FastTrack is improved by 31% on average.

We present RDIT, a novel dynamic technique to detect data races in multithreaded programs with incomplete trace information, i.e., in the presence of missing events. RDIT is both precise and maximal: it does not report any false alarms and it detects a maximal set of true traces from the observed incomplete trace. RDIT is underpinned by a sound BarrierPair model that abstracts away the missing events by capturing the invocation data of their enclosing methods. By making the least conservative abstraction that a missing method introduces synchronization only when it has a memory address in scope that overlaps with other events or other missing methods, and by formulating maximal thread causality as logical constraints, RDIT guarantees to precisely detect races with maximal capability. RDIT has been applied in seven real-world large concurrent systems and has detected dozens of true races with zero false alarms. Comparatively, existing algorithms such as Happens-Before, Causal- Precedes, and Maximal-Causality which are known to be precise all report many false alarms when missing synchronizations.

We present RDIT, a novel dynamic algorithm to precisely detect data races in multi-threaded programs with incomplete trace information -- the presence of missing events. RDIT enhances the classical Happens-Before algorithm by relaxing the need to collect the full execution trace, while still guaranteeing full precision. The key idea behind RDIT is to abstract away the missing events by capturing the invocation data of the missing methods. This provides valuable information to approximate the possible synchronization behavior introduced by the missing events. By making the least conservative approximation that two missing methods introduce synchronization only when they access common data, RDIT guarantees to detect a maximal set of true races from the information available. We have conducted a preliminary study of RDIT on a real system and our results show that RDIT is promising; it detects no false positive when events are missed, whereas Happens-Before reports many.