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. In contrast, software-only schemes work on commodity hardware, but suffer from excessive performance overhead and huge logs. In this article, we present the design and implementation of a novel system, Samsara, which uses the hardware-assisted virtualization (HAV) extensions to achieve efficient deterministic replay without requiring any hardware modification. Unlike prior software schemes which trace every single memory access to record interleaving, Samsara leverages HAV 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. 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 2.1x on average.

Abstract We introduce a scheme for constructing a Catmull‐Clark subdivision surface that interpolates the vertices of a quadrilateral mesh with arbitrary topology. The basic idea here is to progressively modify the vertices of an original mesh to generate a new control mesh whose limit surface interpolates all vertices in the original mesh. The scheme is applicable to meshes with any size and any topology, and it has the advantages of both a local scheme and a global scheme.