ShrinkWrap: E cient Dynamic Race Detection for Array-Intensive Programs

We explore a new technique for e cient dynamic race detection on programs using arrays intensively. Standard techniques lead to redundant operations and redundant representations in many common cases. For these common cases, we design dynamic compression methods that eliminate this redundancy. Finally, we implement our techniques in a prototype tool called ShrinkWrap, which is built as an extension to a state-of-the-art precise dynamic race detector. We evaluate the performance and precision of ShrinkWrap on a suite of benchmark programs. We show that our prototype can improve performance dramatically when the target program accesses arrays in a pattern we recognize. The vast majority of the accesses that must be checked by the underlying race detector can be eliminated on almost half of our benchmark programs. However, we also nd that our prototype is not always as time e cient as one might expect given the number of accesses eliminated.