Proving Data Race Freedom in Task Parallel Programs Using a Weaker Partial Order

Abstract

Task parallel programming models such as Habanero Java help developers write idiomatic parallel programs and avoid common errors. Data race freedom is a desirable property for task parallel programs but is difficult to prove because every possible execution of the program must be considered. A partial order over events of an observed program execution induces an equivalence class of executions that the program may also produce. The Does-not-Commute (DC) relation is an efficiently computable partial order used for data race detection. As a relatively weak partial order, the DC relation can represent relatively large equivalence classes of program executions. However, some of these executions may be infeasible, thus leading to false data race reports. The contribution of this paper is a mechanized proof that the DC relation is actually sound for commonly used task parallel programming models. Sound means that the first data race identified by the DC relation is guaranteed to be a real data race. A prototype analysis in the Java Pathfinder model checker shows that the DC relation can significantly reduce the number of explored states required to prove data race freedom in Habanero Java programs. In this application, the search for data race using the DC relation is both sound and complete.