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PMSort: An adaptive sorting engine for persistent memory
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DOI：10.1016/j.sysarc.2021.102279.png)
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Abstract
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Emerging persistent memory (PM, also termed as non-volatile memory) technologies can promise large capacity, non-volatility, byte-addressability and DRAM-comparable access latency. A host of PM-based storage systems and applications that store and access data directly in PM have been inspired by such amazing features. Sorting is an important function for many systems, but how to optimize sorting for PM-based systems has not been systematically studied. In this paper, we conduct extensive experiments for many existing sorting methods, including both conventional sorting algorithms adapted for PM and recently-proposed PM-friendly sorting techniques, on a DRAM and real PM hybrid platform. The results indicate that these sorting methods all have drawbacks for various workloads. Some of the results are even counterintuitive compared to running on a DRAM-simulated platform in their papers. To the best of our knowledge, we are the first to perform a systematic study on the sorting issue for persistent memory. Based on our study, we summarize principles on selecting the optimal algorithms and propose an adaptive sorting engine called PMSort to perform the selecting operation and reduce failure recovery overhead in PM. We conduct extensive experiments and show that PMSort can select the best sorting algorithm in a variety of cases.
Keywords:
Persistent memory
Sorting algorithm
Wear-leveling
Failure recovery
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