HybridPersist: A Compiler Support for User-Friendly and Efficient PM Programming
Yiyu Zhang, Yongzhi Wang, Yanfeng Gao, Xuandong Li, Zhiqiang Zuo
Abstract
Persistent memory (PM), with its data persistence, has found widespread applications. However, programmers have to manually annotate PM operations in programming to achieve crash consistency, which is labor-intensive and error-prone. In this paper, to alleviate the burden of programming PM applications, we develop HybridPersist, a compiler support for user-friendly and efficient PM programming. On the one hand, HybridPersist automatically achieves crash consistency, minimally intruding on programmers with negligible annotations. On the other hand, it enhances both performance and correctness of PM programs through a series of dedicated analysis passes. The evaluations on well-known benchmarks validate that HybridPersist offers superior programming productivity and runtime performance compared to the state-of-the-art.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get 03513fe5-8fc4-4c4b-a73a-31b527908228Related papers
- Yashme: detecting persistency racesHamed Gorjiara, Guoqing Harry Xu, Brian DemskyASPLOS 2022 · 18 citations
- Automated Insertion of Flushes and Fences for PersistencyYutong Guo, Weiyu Luo, Brian DemskyASE 2025 · 1 citation
- Fast, flexible, and comprehensive bug detection for persistent memory programsBang Di, Jiawen Liu, Hao Chen, Dong LiASPLOS 2021 · 37 citations
- Checking robustness to weak persistency modelsHamed Gorjiara, Weiyu Luo, Alex Lee, Guoqing Harry Xu et al.PLDI 2022 · 13 citations
- Cross-Failure Bug Detection in Persistent Memory ProgramsSihang Liu, Korakit Seemakhupt, Yizhou Wei, Thomas F. Wenisch et al.ASPLOS 2020 · 60 citations
