DMA-Assisted I/O for Persistent Memory

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-03-14 DOI:10.1109/TPDS.2024.3373003
Dingding Li;Weijie Zhang;Mianxiong Dong;Kaoru Ota
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Abstract

Modern local persistent memory (PM) file systems often rely on CPU-based memory copying for data transfer between DRAM and PM, resulting in significant CPU resource consumption. While some nascent systems explore DMA (direct memory access) as an alternative for improved efficiency, the intricacies and trade-offs remain obscure. This paper investigates the feasibility of DMA for PM I/O and argues that it is not a straightforward replacement for CPU-based methods. Two key limitations hinder the direct adoption: poor performance for small data and limited bandwidth. To relieve these issues, we propose PM-DMA, a novel I/O mechanism that leverages the strengths of both CPU and DMA. It incorporates three key components: (1) L-Switch, seamlessly switches between CPU and DMA modes based on workload characteristics, maximizing performance; (2) D-Pool, reduces DMA setup overhead, improving responsiveness; (3) P-Mode, allows servicing requests through multiple channels, even hybrid CPU-DMA ones, for enhanced throughput. We implemented PM-DMA on two well-known PM file systems, NOVA and WineFS, utilizing Intel I/OAT technology. Our experimental results demonstrate substantial CPU consumption reductions across diverse workloads. Notably, under heavy load, PM-DMA delivers up to a $10.4\times$ performance improvement.
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持久性内存的 DMA 辅助 I/O
现代本地持久内存(PM)文件系统通常依赖于基于 CPU 的内存复制,在 DRAM 和 PM 之间进行数据传输,从而导致大量 CPU 资源消耗。虽然一些新生系统探索用 DMA(直接内存访问)作为提高效率的替代方法,但其中的复杂性和利弊权衡仍然模糊不清。本文研究了 DMA 用于 PM I/O 的可行性,并认为它不能直接替代基于 CPU 的方法。有两个关键限制阻碍了直接采用:小数据性能差和带宽有限。为了解决这些问题,我们提出了 PM-DMA,一种利用 CPU 和 DMA 优点的新型 I/O 机制。它包含三个关键部分:(1) L-Switch,根据工作负载特征在 CPU 和 DMA 模式之间无缝切换,从而最大限度地提高性能;(2) D-Pool,减少 DMA 设置开销,提高响应速度;(3) P-Mode,允许通过多个通道(甚至是 CPU-DMA 混合通道)为请求提供服务,从而提高吞吐量。我们利用英特尔 I/OAT 技术,在两个著名的 PM 文件系统 NOVA 和 WineFS 上实现了 PM-DMA。我们的实验结果表明,在不同的工作负载下,CPU 消耗都有大幅降低。值得注意的是,在重负载情况下,PM-DMA 可带来高达 10.4 美元/次的性能提升。
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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