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
Abstract
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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