Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Predictive evaluation of partitioning algorithms through runtime modelling

Tools
- Tools
+ Tools

Bunt, Richard A., Wright, Steven A., Jarvis, Stephen A., Street, Matthew and Ho, Yoon K. (2016) Predictive evaluation of partitioning algorithms through runtime modelling. In: High Performance Computing, Data, and Analytics (HiPC'16), Hyderabad, India, 19-22 Dec 2016. Published in: 2016 IEEE 23rd International Conference on High Performance Computing (HiPC) pp. 351-361. ISBN 9781509054121.

[img] PDF
WRAP_081016-model_extension_paper.pdf - Accepted Version - Requires a PDF viewer.

Download (585Kb)
Official URL: http://doi.org/10.1109/HiPC.2016.048

Request Changes to record.

Abstract

Performance modelling unstructured mesh codes is a challenging process, due to the difficulty of capturing their memory access patterns, and their communication patterns at varying scale. In this paper we first develop extensions to an existing runtime performance model, aimed at overcoming the former, which we validate on up to 1,024 cores of a Haswellbased cluster, using both a geometric partitioning algorithm and ParMETIS to partition the input deck, with a maximum absolute runtime error of 12.63% and 11.55% respectively. To overcome the latter, we develop an application representative of the mesh partitioning process internal to an unstructured mesh code. This application is able to generate partitioning data that is usable with the performance model to produce predicted application runtimes within 7.31% of those produced using empirically collected data. We then demonstrate the use of the performance model by undertaking a predictive comparison among several partitioning algorithms on up to 30,000 cores. Additionally, we correctly predict the ineffectiveness of the geometric partitioning algorithm at 512 and 1024 cores.

Item Type: Conference Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): High performance computing, Fluid dynamics
Journal or Publication Title: 2016 IEEE 23rd International Conference on High Performance Computing (HiPC)
Publisher: IEEE
ISBN: 9781509054121
Official Date: 2016
Dates:
DateEvent
2016Published
6 September 2016Accepted
Page Range: pp. 351-361
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Rolls-Royce Group plc, Bull (Firm)
Conference Paper Type: Paper
Title of Event: High Performance Computing, Data, and Analytics (HiPC'16)
Type of Event: Conference
Location of Event: Hyderabad, India
Date(s) of Event: 19-22 Dec 2016

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us