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

Ruirui C. Huang
Erik Halberg
Andrew Ferraiuolo
G. Edward Suh
Proceedings of the 20th International Symposium on High Performance Computer Architecture (HPCA)(2014)


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.