Efficient Data Race Detection for C/C++ Programs Using Dynamic Granularity

Abstract

To detect races precisely without false alarms, vector clock based race detectors can be applied if the overhead in time and space can be contained. This is indeed the case for the applications developed in object-oriented programming language where objects can be used as detection units. On the other hand, embedded applications, often written in C/C++, necessitate the use of fine-grained detection approaches that lead to significant execution overhead. In this paper, we present a dynamic granularity algorithm for vector clock based data race detectors. The algorithm exploits the fact that neigh boring memory locations tend to be accessed together and can share the same vector clock archiving dynamic granularity of detection. The algorithm is implemented on top of Fast Track and uses Intel PIN tool for dynamic binary instrumentation. Experimental results on benchmarks show that, on average, the race detection tool using the dynamic granularity algorithm is 43% faster than the Fast Track with byte granularity and is with 60% less memory usage. Comparison with existing industrial tools, Val grind DRD and Intel Inspector XE, also suggests that the proposed dynamic granularity approach is very viable.