非易失性存储器上高性能计算I/O的性能评估与建模

W. Liu, Kai Wu, Jialin Liu, F. Chen, Dong Li
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引用次数: 9

摘要

高性能计算应用对I/O性能和存储能力提出了很高的要求。新兴的非易失性存储器(NVM)技术为高性能计算应用程序提供了低延迟、高带宽和持久性。然而,现有的I/O堆栈是基于基于磁盘存储的假设来设计和优化的。为了有效地使用NVM,我们必须重新审视现有的高性能计算(HPC) I/O子系统,将NVM适当地集成到其中。使用NVM作为快速存储,先前关于存储(例如硬盘驱动器)性能较差的假设不再成立。由慢速存储引起的性能问题可以得到缓解;现有的缩小存储和CPU之间性能差距的机制可能是不必要的,并且会导致很大的开销。因此,要充分理解将NVM引入HPC软件堆栈的影响,需要进行彻底的性能研究。在本文中,我们分析和建模了NVM作为块设备的I/O密集型HPC应用程序的性能。本文从三个方面对性能进行了研究:(1)NVM对传统页面缓存性能的影响;(2) MPI个人I/O和POSIX I/O之间的性能比较;(3) NVM对集体I/O性能的影响。我们揭示了页面缓存的减少影响,MPI单个I/O和POSIX I/O之间的微小性能差异,以及由于不必要的数据变换而导致的NVM上的集体I/O的性能劣势。我们还对MPI集体I/O的性能进行了建模,并研究了数据变换、存储性能和I/O访问模式之间的复杂交互。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Evaluation and Modeling of HPC I/O on Non-Volatile Memory
HPC applications pose high demands on I/O performance and storage capability. The emerging non-volatile memory (NVM) techniques offer low- latency, high bandwidth, and persistence for HPC applications. However, the existing I/O stack are designed and optimized based on an assumption of disk-based storage. To effectively use NVM, we must re-examine the existing high performance computing (HPC) I/O sub-system to properly integrate NVM into it. Using NVM as a fast storage, the previous assumption on the inferior performance of storage (e.g., hard drive) is not valid any more. The performance problem caused by slow storage may be mitigated; the existing mechanisms to narrow the performance gap between storage and CPU may be unnecessary and result in large overhead. Thus fully understanding the impact of introducing NVM into the HPC software stack demands a thorough performance study. In this paper, we analyze and model the performance of I/O intensive HPC applications with NVM as a block device. We study the performance from three perspectives: (1) the impact of NVM on the performance of traditional page cache; (2) a performance comparison between MPI individual I/O and POSIX I/O; and (3) the impact of NVM on the performance of collective I/O. We reveal the diminishing effects of page cache, minor performance difference between MPI individual I/O and POSIX I/O, and performance disadvantage of collective I/O on NVM due to unnecessary data shuffling. We also model the performance of MPI collective I/O and study the complex interaction between data shuffling, storage performance, and I/O access patterns.
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