FRESA: A Frequency-Sensitive Sampling-Based Approach for Data Race Detection

Concurrent programs are difficult to debug due to the inherent concurrence and indeterminism. One of the problems is race conditions. Previous work on dynamic race detection includes fast but imprecise methods that report false alarms, and slow but precise ones that never report false alarms. Some researchers have combined these two methods. However, the overhead is still massive. This paper exploits the insight that full record on detector is unnecessary in most cases. Even prior sampling method has something to do to reduce overhead with precision guaranteed. That is, we can use a frequency-sensitive sampling approach. With our model on sampling dispatch, we can drop most unnecessary detection overhead. Experiment results on DaCapo benchmarks show that our heuristic sampling race detector is performancefaster and overhead-lower than traditional race detectors with no loss in precision, while never reporting false alarms.