A survey of application memory usage on a national supercomputer: An analysis of memory requirements on ARCHER

Andy Turner*, Simon McIntosh-Smith

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)
167 Downloads (Pure)

Abstract

In this short paper we set out to provide a set of modern data on the actual memory per core and memory per node requirements of the most heavily used applications on a contemporary, national-scale supercomputer. This report is based on data from all jobs run on the UK national supercomputing service, ARCHER, a 118,000 core Cray XC30, in the 1 year period from 1st July 2016 to 30th June 2017 inclusive. Our analysis shows that 80% of all usage on ARCHER has a maximum memory use of 1 GiB/core or less (24 GiB/node or less) and that there is a trend to larger memory use as job size increases. Analysis of memory use by software application type reveals differences in memory use between periodic electronic structure, atomistic N-body, grid-based climate modelling, and grid-based CFD applications. We present an analysis of these differences, and suggest further analysis and work in this area. Finally, we discuss the implications of these results for the design of future HPC systems, in particular the applicability of high bandwidth memory type technologies.

Original languageEnglish
Title of host publicationHigh Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation
Subtitle of host publication8th International Workshop, Proceedings
EditorsS Jarvis, S Wright, S Hammond
PublisherSpringer Verlag
Pages250-260
Number of pages11
ISBN (Electronic)9783319729718
ISBN (Print)9783319729701
DOIs
Publication statusPublished - 1 Jan 2018
Event8th International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computing Systems, PMBS 2017 - [state] CO, United States
Duration: 13 Nov 201713 Nov 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10724 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computing Systems, PMBS 2017
CountryUnited States
City[state] CO
Period13/11/1713/11/17

Keywords

  • HPC
  • Memory
  • Profiling

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