A Parallel FastTrack Data Race Detector on Multi-core Systems

Detecting data races in multithreaded programs is critical to ensure the correctness of the programs. To discover data races precisely without false alarms, dynamic detection approaches are often applied. However, the overhead of the existing dynamic detection approaches, even with recent innovations, is still substantially high. In this paper, we present a simple but efficient approach to parallelize data race detection in multicore SMP (Symmetric Multiprocessing) machines. In our approach, data access information needed for dynamic detection is collected at application threads and passed to detection threads. The access information is distributed in a way that the operation performed by each detection thread is independent of that of other detection threads. As a consequence, the overhead caused by locking operations in data race detection can be alleviated and multiple cores can be fully utilized to speed up and scale up the detection. Furthermore, each detection thread deals with only its own assigned memory access region rather than the whole address space. The executions of detection threads can exploit the spatial locality of accesses leading to an improved cache performance. We have applied our parallel approach on the FastTrack algorithm and demonstrated the validity of our approach on an Intel Xeon machine. Our experimental results show that the parallel FastTrack detector, on average, runs 2.2 times faster than the original FastTrack detector on the 8 core machine.