An Efficient Algorithm for On-the-Fly Data Race Detection Using an Epoch-Based Technique

Abstract

Data races represent the most notorious class of concurrency bugs in multithreaded programs. To detect data races precisely and efficiently during the execution of multithreaded programs, the epoch-based FASTTRACK technique has been employed. However, FASTTRACK has time and space complexities that depend on the maximum parallelism of the program to partially maintain expensive data structures, such as vector clocks. This paper presents an efficient algorithm, called iFT, that uses only the epochs of the access histories. Unlike FASTTRACK, our algorithm requires O(1) operations to maintain an access history and locate data races, without any switching between epochs and vector clocks. We implement this algorithm on top of the Pin binary instrumentation framework and compare it with other on-the-fly detection algorithms, including FASTTRACK, which uses a state-of-the-art happens-before analysis algorithm. Empirical results using the PARSEC benchmark show that iFT reduces the average runtime and memory overhead to 84% and 37%, respectively, of those of FASTTRACK.