Samsara: Efficient Deterministic Replay in Multiprocessor Environments with Hardware Virtualization Extensions

Deterministic replay, which provides the ability to travel backward in time and reconstruct the past execution flow of a multiprocessor system, has many prominent applications. Prior research in this area can be classified into two categories: hardware-only schemes and software-only schemes. While hardware-only schemes deliver high performance, they require significant modifications to the existing hardware which makes them difficult to deploy in real systems. In contrast, software-only schemes work on commodity hardware, but suffer from excessive performance overhead and huge logs caused by tracing every single memory access in the software layer.

In this paper, we present the design and implementation of a novel system, Samsara, which uses the hardware-assisted virtualization (HAV) extensions to achieve efficient and practical deterministic replay without requiring any hardware modification. Unlike prior software schemes which trace every single memory access to record interleaving, Samsara leverages the HAV extensions on commodity processors to track the read-set and write-set for implementing a chunk-based recording scheme in software. By doing so, we avoid all memory access detections, which is a major source of overhead in prior works. We implement and evaluate our system in KVM on commodity Intel Haswell processor. Evaluation results show that compared with prior software-only schemes, Samsara significantly reduces the log file size to 1/70th on average, and further reduces the recording overhead from about 10x, reported by state-of-the-art works, to 2.3x on average.