Array Shadow State Compression for Precise Dynamic Race Detection (T)

Abstract

Precise dynamic race detectors incur significant time and space overheads, particularly for array-intensive programs, due to the need to store and manipulate analysis (or shadow) state for every element of every array. This paper presents SlimState, a precise dynamic race detector that uses an adaptive, online algorithm to optimize array shadow state representations. SlimState is based on the insight that common array access patterns lead to analogous patterns in array shadow state, enabling optimized, space efficient representations of array shadow state with no loss in precision. We have implemented SlimState for Java. Experiments on a variety of benchmarks show that array shadow compression reduces the space and time overhead of race detection by 27% and 9%, respectively. It is particularly effective for array-intensive programs, reducing space and time overheads by 35% and 17%, respectively, on these programs.