Performance measurements of supercomputing and cloud storage solutions

Michael Jones, Jeremy Kepner, William Arcand, David Bestor, Bill Bergeron, Vijay Gadepally, Michael Houle, Matthew Hubbell, Peter Michaleas, Andrew Prout, Albert Reuther, Siddharth Samsi, Paul Monticiollo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Increasing amounts of data from varied sources, particularly in the fields of machine learning and graph analytics, are causing storage requirements to grow rapidly. A variety of technologies exist for storing and sharing these data, ranging from parallel file systems used by supercomputers to distributed block storage systems found in clouds. Relatively few comparative measurements exist to inform decisions about which storage systems are best suited for particular tasks. This work provides these measurements for two of the most popular storage technologies: Lustre and Amazon S3. Lustre is an open-source, high performance, parallel file system used by many of the largest supercomputers in the world. Amazon's Simple Storage Service, or S3, is part of the Amazon Web Services offering, and offers a scalable, distributed option to store and retrieve data from anywhere on the Internet. Parallel processing is essential for achieving high performance on modern storage systems. The performance tests used span the gamut of parallel I/O scenarios, ranging from single-client, single-node Amazon S3 and Lustre performance to a large-scale, multi-client test designed to demonstrate the capabilities of a modern storage appliance under heavy load. These results show that, when parallel I/O is used correctly (i.e., many simultaneous read or write processes), full network bandwidth performance is achievable and ranged from 10 gigabits/s over a 10 GigE S3 connection to 0.35 terabits/s using Lustre on a 1200 port 10 GigE switch. These results demonstrate that S3 is well-suited to sharing vast quantities of data over the Internet, while Lustre is well-suited to processing large quantities of data locally.

Original languageEnglish
Title of host publication2017 IEEE High Performance Extreme Computing Conference, HPEC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538634721
DOIs
StatePublished - Oct 30 2017
Externally publishedYes
Event2017 IEEE High Performance Extreme Computing Conference, HPEC 2017 - Waltham, United States
Duration: Sep 12 2017Sep 14 2017

Publication series

Name2017 IEEE High Performance Extreme Computing Conference, HPEC 2017

Conference

Conference2017 IEEE High Performance Extreme Computing Conference, HPEC 2017
Country/TerritoryUnited States
CityWaltham
Period09/12/1709/14/17

Keywords

  • Amazon Simple Storage Service
  • High Performance Computing
  • High Performance Storage
  • Lustre
  • MIT Super-Cloud

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