Scalable concurrency debugging with distributed graph processing

Existing constraint-solving-based technique enables an efficient and high-coverage concurrency debugging. Yet, there remains a significant gap between the state of the art and the state of the programming practices for scaling to handle long-running execution of programs. In this paper, we revisit the scalability problem of state-of-the-art constraint-solving-based technique. Our key insight is that concurrency debugging for many real-world bugs can be turned into a graph traversal problem. We therefore present GraphDebugger, a novel debugging framework to enable the scalable concurrency analysis on program graphs via a tailored graph-parallel analysis in a distributed environment. It is verified that GraphDebugger is more capable than CLAP in reproducing the real-world bugs that involve a complex concurrency analysis. Our extensive evaluation on 7 real-world programs shows that, GraphDebugger (deployed on an 8-node EC2 like cluster) is significantly efficient to reproduce concurrency bugs within 1∼8 minutes while CLAP does so with 1∼30 hours, or even without returning solutions.