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Cornell University

Low-Overhead and High Coverage Run-Time Race Detection Through Selective Meta-Data Management


This paper presents an efficient hardware architecture that enables run-time data race detection with high coverage and minimal performance overhead. Run-time race detectors often rely on the happens-before vector clock algorithm for accuracy, yet suffer from either non-negligible performance overhead or low detection coverage due to a large amount of meta-data. Based on the observation that most of data races happen between close-by accesses, we introduce an optimization to selectively store meta-data only for recently shared memory locations and decouple meta-data storage from regular data storage such as caches. Experiments show that the proposed scheme enables run-time race detection with a minimal impact on performance (4.8% overhead on average) with very high detection coverage (over 99%). Furthermore, this architecture only adds a small amount of on-chip resources for race detection: a 13-KB buffer per core and a 1-bit tag per data cache block.

In Proceedigns of the 20th International Symposium on High Performance Computer Architecture (HPCA)