Disk-directed I/O for MIMD multiprocessors

Author:

Kotz David1

Affiliation:

1. Dartmouth College, Hanover, NH

Abstract

Many scientific applications that run on today's multiprocessors, such as weather forecasting and seismic analysis, are bottlenecked by their file-I/O needs. Even if the multiprocessor is configured with sufficient I/O hardware, the file system software often fails to provide the available bandwidth to the application. Although libraries and enhanced file system interfaces can make a significant improvement, we believe that fundamental changes are needed in the file server software. We propose a new technique, disk-directed I/O, to allow the disk servers to determine the flow of data for maximum performance. Our simulations show that tremendous performance gains are possible both for simple reads and writes and for an out-of-core application. Indeed, our disk-directed I/O technique provided consistent high performance that was largely independent of data distribution and obtained up to 93% of peak disk bandwidth. It was as much as 18 times faster than either a typical parallel file system or a two-phase-I/O library.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference57 articles.

Cited by 45 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Improving I/O Performance for Exascale Applications Through Online Data Layout Reorganization;IEEE Transactions on Parallel and Distributed Systems;2022-04-01

2. Block I/O Scheduling on Storage Servers of Distributed File Systems;Journal of Grid Computing;2018-01-17

3. Reducing I/O variability using dynamic I/O path characterization in petascale storage systems;The Journal of Supercomputing;2016-11-01

4. Collective input/output under memory constraints;The International Journal of High Performance Computing Applications;2014-12-18

5. Performance model-directed data sieving for high-performance I/O;The Journal of Supercomputing;2014-09-10

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3