Enforcing Crash Consistency of Scientific Applications in Non-Volatile Main Memory Systems

Abstract

To fully leverage the emerging non-volatile main memory (NVMM) for high-performance computing, programmers need efficient data structures that are aware of NVMM memory models and provide crash consistency. Manual creation of NVMM-aware persistent data structures requires a deep understanding of how to create persistent snapshots of memory objects corresponding to the data structures and substantial code modification, which makes it very difficult to use in its manual form even for experienced programmers. To simplify the process, we design a compiler-assistant technique, NVPath. With the aid of compilers, it automatically generates NVMM-aware persistent data structures that provide the same level of guarantee of crash consistency compared to the baseline code. Compiler-assistant code annotation and transformation are general and can be applied to applications using various data structures. Furthermore, it is a gray-box technique which requires minimum users' input. Finally, it keeps the baseline code structure for good readability and maintenance. Our experimental results with real-world scientific applications (e.g., matrix multiplication, LU decomposition, adaptive-mesh refinement, and page ranking) show that the performance of annotated programs is commensurate with the version using the manual code transformation on the Titan supercomputer.