Data Direct I/O Characterization for Future I/O System Exploration

Mohammad Alian, Yifan Yuan, Jie Zhang, Ren Wang, Myoungsoo Jung, Nam Sung Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

I/O performance plays a critical role in the overall performance of modern servers. The emergence of ultra high-speed I/O devices makes the data movement between processors, main memory, and devices a major performance bottleneck. Conventionally, the main memory is used as an intermediate buffer between the processor and I/O devices and I/O devices cannot directly access processor side caches. Data Direct I/O (DDIO) technology aims to reduce the memory bandwidth utilization by enabling the I/O devices to leverage Last Level Cache (LLC) as the intermediate buffer. Our experimental results show that DDIO can completely eliminate memory bandwidth utilization while running network-or storage-intensive applications. However, when modeling the I/O subsystem using architectural simulators, DDIO is often ignored, which can result in inaccurate assessments about the I/O and memory sub system of emerging and future large-scale computer systems. In this paper, we provide a detailed background on DDIO technology in Intel server processors. Then we present our cycle-accurate I/O subsystem model in gem5 simulator that can be configured to model DDIO. We verify our model against baseline gem5 and validate it by comparing its results against a physical computer system.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages160-169
Number of pages10
ISBN (Electronic)9781728147987
DOIs
Publication statusPublished - 2020 Aug
Event2020 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2020 - Boston, United States
Duration: 2020 Aug 232020 Aug 25

Publication series

NameProceedings - 2020 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2020

Conference

Conference2020 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2020
Country/TerritoryUnited States
CityBoston
Period20/8/2320/8/25

Bibliographical note

Funding Information:
VIII. ACKNOWLEDGMENTS We would like to thank the anonymous reviewers for their comments. We also thank Anil Vasudevan, Robert Blanken-ship, Bin Li and Charlie Tai, for their insightful feedback and technical support. This work is supported by funding from National Science Foundation (No. CNS-1705047).

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

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