SLIMFAST: Reducing Metadata Redundancy in Sound and Complete Dynamic Data Race Detection

Data races are one of the main culprits behind the complexity of multithreaded programming. Existing data race detectors require large amounts of metadata for each program variable to perform their analyses. The SLIMFAST system exploits the insight that there is a large amount of redundancy in this metadata: many program variables often have identical metadata state. By sharing metadata across variables, a large reduction in space usage can be realized. SLIMFAST uses immutable metadata to safely support metadata sharing across threads while also accelerating concurrency control. SLIMFAST's lossless metadata compression achieves these benefits while preserving soundness and completeness. Across a range of benchmarks from Java Grande, DaCapo, NAS Parallel Benchmarks and Oracle's BerkeleyDB Java Edition, SLIMFAST is able to reduce memory consumption by 1.83x on average, and up to 4.90x for some benchmarks, compared to the state-of-the-art FASTTRACK system. By improving cache locality and simplifying concurrency control, SLIMFAST also accelerates data race detection by 1.40x on average, and up to 8.8x for some benchmarks, compared to FASTTRACK.