GBRAD: A General Framework to Evaluate Design Strategies for Hybrid Race Detection

Abstract

Data race detection is a method in testing multithreaded programs to ensure their reliability against concurrency errors. In this paper, we present the GBRAD framework to support the initialization of various hybrid race detection techniques, which also supports the evaluation of these strategies at two decision points based on two major design factors of hybrid race detectors. In the GBRAD frame-work, one decision point consists of six skipping strategies and another decision point consists of eight reduction strategies. By combining these strategies, 48 hybrid detection techniques are initialized. We report a controlled experiment on the PARSEC benchmark suite as well as four real-world applications to evaluate these 48 techniques and their strategies in terms of runtime slowdown, memory overhead, and race detection effectiveness. The experiment identified 9 previously unknown techniques that are comparable to the state-of-the-art hybrid race detection technique.