Tuning lock-based multicore program based on sliding windows to tolerate data race

Abstract

Because in-house debugging and test are difficult to discover all potential data races in multicore programs, it is necessary and significant to tolerate the potential data races in the production-run phase to secure the correct execution. However, the existing tolerating methods are limited to some kinds of data races. This paper proposes a new data-race tolerating approach, which can detect and adjust the data races whether it is in the protection of critical section or lack of protection to improve the correctness of multicore programs. It uses sliding windows to accommodate the memory instructions in critical section or recent memory instructions lack of protection and detects the potential data races which are more likely to cause errors. Then, by delaying the critical reversion points, data races are adjusted to reduce the probability of software failure. To implement the tolerating approach, the current multicore processor need not change its original cache coherence protocol and just adds very little hardware. Simulation results show that it brings low hardware, low bandwidth overhead, and negligible slowdown.