Redesigning the Sorting Engine for Persistent Memory

Abstract: Emerging persistent memory (PM, also termed as nonvolatile memory) technologies can promise large capacity, non-volatility, byte-addressability and DRAM-comparable access latency. Such amazing features have inspired a host of PM-based storage systems and applications that store and access data directly in PM. Sorting is an important function for many systems, but how to optimize sorting for PM-based systems has not been systematically studied yet. 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 real PM 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 propose an adaptive sorting engine, namely SmartSort, to optimize the sorting performance for different conditions. The experimental results demonstrate that SmartSort remarkably outperforms existing sorting methods in a variety of cases.