Musoles, C.F., Coca, D. orcid.org/0000-0003-2878-2422 and Richmond, P. orcid.org/0000-0002-4657-5518 (2019) HyperPRAW : architecture-aware hypergraph restreaming partition to improve performance of parallel applications running on high performance computing systems. In: ICPP 2019: Proceedings of the 48th International Conference on Parallel Processing. ICPP 2019: 48th International Conference on Parallel Processing, 05-08 Aug 2019, Kyoto, Japan. ACM Digital Library , pp. 1-10. ISBN 9781450362955
Abstract
High Performance Computing (HPC) demand is on the rise, particularly for large distributed computing. HPC systems have, by design, very heterogeneous architectures, both in computation and in communication bandwidth, resulting in wide variations in the cost of communications between compute units. If large distributed applications are to take full advantage of HPC, the physical communication capabilities must be taken into consideration when allocating workload. Hypergraphs are good at modelling total volume of communication in parallel and distributed applications. To the best of our knowledge, there are no hypergraph partitioning algorithms to date that are architecture-aware. We propose a novel restreaming hypergraph partitioning algorithm (HyperPRAW) that takes advantage of peer to peer physical bandwidth profiling data to improve distributed applications performance in HPC systems. Our results show that not only the quality of the partitions achieved by our algorithm is comparable with state-of-the-art multilevel partitioning, but that the runtime performance in a synthetic benchmark is significantly reduced in 10 hypergraph models tested, with speedup factors of up to 14x.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2019 Association for Computing Machinery. This is an author-produced version of a paper subsequently published in ICPP 2019. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | datasets; neural networks; gaze detection; text tagging |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number Biotechnology and Biological Sciences Research Council BB/M025527/1 Engineering and Physical Science Research Council EP/N018869/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Feb 2020 09:38 |
Last Modified: | 12 Feb 2020 10:46 |
Status: | Published |
Publisher: | ACM Digital Library |
Refereed: | Yes |
Identification Number: | 10.1145/3337821.3337876 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:155305 |