RaceTrack: Efficient Detection of Data Race Conditions via Adaptive Tracking
Yuan Yu (Microsoft Research)
- Place: E2, Rm 392
- Time: 2-3:10pm, Wednesday, February 8, 2006
Abstract
Bugs due to data races in multithreaded programs often exhibit non-deterministic symptoms and are notoriously difficult to find. In this talk, I will describe RaceTrack, a dynamic race detection tool that tracks the actions of a program and reports a warning whenever a suspicious pattern of activity has been observed. RaceTrack uses a novel hybrid detection algorithm and employs an adaptive approach that automatically directs more effort to areas that are more suspicious, thus providing more accurate warnings for much less overhead. We implemented RaceTrack inside the virtual machine of Microsoft's Common Language Runtime (product version v1.1.4322) and monitored several major, real-world applications directly out-of-the-box, without any modification. RaceTrack instrumentation resulted in a slowdown of 3x to 4x on memory-intensive programs and typically much less than 2x on other programs. I will also talk about future plans for the tool.
Joint work with Tom Rodeheffer and Wei Chen.