Abstract

Data races indicate serious concurrency bugs such as order, atomicity, and sequential consistency violations. Races are difficult to find and fix, often manifesting only in deployment. The frequency of these bugs will likely increase as software adds parallelism to exploit multicore hardware. Unfortunately, sound and precise race detectors slow programs by factors of eight or more, which is too expensive to deploy. This paper presents a precise, low-overhead sampling-based data race detector called PACER. PACER makes a proportionality guarantee: it detects any race at a rate equal to the sampling rate, by finding races whose first access occurs during a global sampling period. During sampling, PACER tracks all accesses using the sound and precise FASTTRACK algorithm. In non-sampling periods, PACER discards sampled access information that cannot be part of a reported race, and PACER simplifies tracking of the happens-before relationship, yielding near-constant, instead of linear, overheads. Experimental results confirm our design claims: time and space overheads scale with the sampling rate, and sampling rates of 1-3 % yield overheads low enough to consider in production software. Furthermore, our results suggest PACER reports each race at a rate equal to the sampling rate. The resulting system provides a “get what you pay for ” approach to race detection that is suitable for identifying real, hard-to-reproduce races in deployed systems. 1.

Citations