RaceMob: Crowdsourced Data Race Detection

Some of the worst concurrency problems in multi-threaded systems today are due to data races—these bugs can have messy consequences, and they are hard to diagnose and fix. To avoid the introduction of such bugs, system developers need discipline and good data race detectors; today, even if they have the former, they lack the latter. We present RaceMob, a new data race detector that has both low overhead and good accuracy. RaceMob starts by detecting potential races statically (hence it has few false negatives), and then dynamically validates whether these are true races (hence has few false positives). It achieves low runtime overhead and a high degree of realism by combining real-user crowdsourcing with a new on-demand dynamic data race validation technique. We evaluated RaceMob on ten systems, including Apache, SQLite, and Memcached—it detects data races with higher accuracy than state-of-the-art detectors (both static and dynamic), and RaceMob users experience an average runtime overhead of about 2%, which is orders of magnitude less than the overhead of modern dynamic data race detectors. To the best of our knowledge, Race-Mob is the first data race detector that can both be used always-on in production and provides good accuracy.