Hydra: Efficient Detection of Multiple Concurrency Bugs on Fused CPU-GPU Architecture

Abstract

Detecting concurrency bugs, such as data race, atomicity violation and order violation, is a cumbersome task for programmers. This situation is further being exacerbated due to the increasing number of cores in a single machine and the prevalence of threaded programming models. Unfortunately, many existing software-based approaches usually incur high runtime overhead or accuracy loss, while most hardware-based proposals usually focus on a specific type of bugs and thus are inflexible to detect a variety of concurrency bugs. In this paper, we propose Hydra, an approach that leverages massive parallelism and programmability of fused GPU architecture to simultaneously detect multiple types of concurrency bugs, including data race, atomicity violation and order violation. Hydra instruments and collects program behavior on CPU and transfers the traces to GPU for bug detection through on-chip interconnect. Furthermore, to achieve high speed, Hydra exploits bloom filter to filter out unnecessary detection traces. Hydra incurs small hardware complexity and requires no changes to internal critical-path processor components such as cache and its coherence protocol, and is with about 1.1% hardware overhead under a 32-core configuration. Experimental results show that Hydra only introduces about 0.35% overhead on average for detecting one type of bugs and 0.92% overhead for simultaneously detecting multiple bugs, yet with the similar detectability of a heavyweight software bug detector (e.g., Helgrind).