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ASPLOS2023顶会

SpecPMT: Speculative Logging for Resolving Crash Consistency Overhead of Persistent Memory

Chencheng Ye, Yuanchao Xu, Xipeng Shen, Yan Sha, Xiaofei Liao, Hai Jin, Yan Solihin

2023年份
13被引次数
2顶会引用

摘要

Crash consistency overhead is a long-standing barrier to the adoption of byte-addressable persistent memory in practice. Despite continuous progress, persistent transactions for crash consistency still incur a 5.6X slowdown, making persistent memory prohibitively costly in practical settings. This paper introduces speculative logging, a new method that forgoes most memory fences and reduces data persistence overhead by logging data values early. This technique enables a novel persistent transaction model, speculatively persistent memory transactions (SpecPMT). Our evaluation shows that SpecPMT reduces the execution time overheads of persistent transactions substantially to just 10%.

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