Adversarial memory for detecting destructive races

Multithreaded programs are notoriously prone to race conditions, a problem exacerbated by the widespread adoption of multi-core processors with complex memory models and cache coherence protocols. Much prior work has focused on static and dynamic analyses for race detection, but these algorithms typically are unable to distinguish destructive races that cause erroneous behavior from benign races that do not. Performing this classification manually is difficult, time consuming, and error prone. This paper presents a new dynamic analysis technique that uses adversarial memory to classify race conditions as destructive or benign on systems with relaxed memory models. Unlike a typical language implementation, which may only infrequently exhibit non-sequentially consistent behavior, our adversarial memory implementation exploits the full freedom of the memory model to return older, unexpected, or stale values for memory reads whenever possible, in an attempt to crash the target program (...

• Type: Conference
• Year: 2010
• Where: PLDI
• Authors: Cormac Flanagan, Stephen N. Freund
• Keywords: Adversarial Memory, Memory Model, PLDI 2010, Race Conditions