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HPC-GAP : engineering a 21st-century High-Performance Computer algebra system

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Behrends_2016_HPC_GAP_CCPE_CC.pdf (3.581Mb)
Date
10/09/2016
Author
Behrends, Reimer
Hammond, Kevin
Janjic, Vladimir
Konovalov, Alexander
Linton, Stephen Alexander
Loidl, Hans-Wolfgang
Maier, Patrick
Trinder, Philip
Keywords
Parallelism
Multicore
High-Performance Computing
Computational algebra
QA75 Electronic computers. Computer science
DAS
BDC
R2C
Metadata
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Abstract
Symbolic computation has underpinned a number of key advances in Mathematics and Computer Science. Applications are typically large and potentially highly parallel, making them good candidates for parallel execution at a variety of scales from multi‐core to high‐performance computing systems. However, much existing work on parallel computing is based around numeric rather than symbolic computations. In particular, symbolic computing presents particular problems in terms of varying granularity and irregular task sizes that do not match conventional approaches to parallelisation. It also presents problems in terms of the structure of the algorithms and data. This paper describes a new implementation of the free open‐source GAP computational algebra system that places parallelism at the heart of the design, dealing with the key scalability and cross‐platform portability problems. We provide three system layers that deal with the three most important classes of hardware: individual shared memory multi‐core nodes, mid‐scale distributed clusters of (multi‐core) nodes and full‐blown high‐performance computing systems, comprising large‐scale tightly connected networks of multi‐core nodes. This requires us to develop new cross‐layer programming abstractions in the form of new domain‐specific skeletons that allow us to seamlessly target different hardware levels. Our results show that, using our approach, we can achieve good scalability and speedups for two realistic exemplars, on high‐performance systems comprising up to 32000 cores, as well as on ubiquitous multi‐core systems and distributed clusters. The work reported here paves the way towards full‐scale exploitation of symbolic computation by high‐performance computing systems, and we demonstrate the potential with two major case studies.
Citation
Behrends , R , Hammond , K , Janjic , V , Konovalov , A , Linton , S A , Loidl , H-W , Maier , P & Trinder , P 2016 , ' HPC-GAP : engineering a 21st-century High-Performance Computer algebra system ' , Concurrency and Computation : Practice and Experience , vol. 28 , no. 13 , pp. 3606-3636 . https://doi.org/10.1002/cpe.3746
Publication
Concurrency and Computation : Practice and Experience
Status
Peer reviewed
DOI
https://doi.org/10.1002/cpe.3746
ISSN
1532-0634
Type
Journal article
Rights
© 2016 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/9325

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