论文标题
破折号：可扩展的哈西在持续记忆上
Dash: Scalable Hashing on Persistent Memory
论文作者
论文摘要
BYTE-ADDRABLE-ADDABLE持续记忆（PM）带来了散布表的潜力，潜在的潜力，廉价的持久性和即时恢复。 Intel Optane DC持久记忆模块（DCPMM）最近出现的进来进一步加速了这一趋势。已经提出了许多新的哈希表设计，但大多数是基于仿真的，并在真实PM上表现出色。它们也是部分解决方案，辅助许多重要特性，特别是良好的可伸缩性，高负载因子和即时恢复。我们提出了DASH，这是一种整体方法，用于在实际PM硬件上构建动态和可扩展的哈希表，并具有上述所有属性。基于破折号，我们改编了两个流行的动态哈希方案（扩展的哈希和线性哈希）。在带有Intel Optane DCPMM的24核机上，我们表明，与最先进的启用仪表板的哈希表相比，无论数据尺寸如何，高达90％的负载系数和即时恢复时间的性能高达90％，即时恢复时间高达90％。
Byte-addressable persistent memory (PM) brings hash tables the potential of low latency, cheap persistence and instant recovery. The recent advent of Intel Optane DC Persistent Memory Modules (DCPMM) further accelerates this trend. Many new hash table designs have been proposed, but most of them were based on emulation and perform sub-optimally on real PM. They were also piece-wise and partial solutions that side-step many important properties, in particular good scalability, high load factor and instant recovery. We present Dash, a holistic approach to building dynamic and scalable hash tables on real PM hardware with all the aforementioned properties. Based on Dash, we adapted two popular dynamic hashing schemes (extendible hashing and linear hashing). On a 24-core machine with Intel Optane DCPMM, we show that compared to state-of-the-art, Dash-enabled hash tables can achieve up to ~3.9X higher performance with up to over 90% load factor and an instant recovery time of 57ms regardless of data size.
