Publications

Conference paper

JAROŠ Jiří and TYRALA Radek. GPU-accelerated Evolutionary Design of the Complete Exchange Communication on Wormhole Networks. In: GECCO '14 Proceedings of the 2014 conference on Genetic and evolutionary computation. New York, NY: Association for Computing Machinery, 2014, pp. 1023-1030. ISBN 978-1-4503-2662-9. Available from: http://dl.acm.org/citation.cfm?id=2576768.2598315

Publication language:

english

Original title:

GPU-accelerated Evolutionary Design of the Complete Exchange Communication on Wormhole Networks

Title (cs):

Akcelerace evolučního návhru kolektivní komunikace Compelte Echange pomocí GPU

Pages:

1023-1030

Proceedings:

GECCO '14 Proceedings of the 2014 conference on Genetic and evolutionary computation

Conference:

Genetic and Evolutionary Computations Conference 2014

Place:

New York, NY, US

Year:

2014

URL:

http://dl.acm.org/citation.cfm?id=2576768.2598315

ISBN:

978-1-4503-2662-9

DOI:

10.1145/2576768.2598315

Publisher:

Association for Computing Machinery

Files:

Keywords

Complete exchange communication, Collective communications,
communication scheduling, evolutionary design, GPU-based acceleration,
multi-GPU systems.

Annotation

The communication overhead is one of the main challenges in the exascale era, where millions of compute cores are expected to collaborate on solving complex jobs. However, many algorithms will not scale since they require complex global communication and synchronisation. In order to perform the communication as fast as possible, contentions, blocking and deadlock must be avoided.
Recently, we have developed an evolutionary tool producing fast and safe communication schedules reaching the lower bound of the theoretical time complexity. Unfortunately, the execution time associated with the evolution process raises up to tens of hours, even when being run on a multi-core processor.
In this paper, we propose a revised implementation accelerated by a single Graphic Processing Unit (GPU) delivering speed-up of 5 compared to a quad-core CPU. Subsequently, we introduce an extended version employing up to 8 GPUs in a shared memory environment offering a speed-up of almost 30. This significantly extends the range of interconnection topologies we can cover.

BibTeX:

@INPROCEEDINGS{
   author = {Ji{\v{r}}{\'{i}} Jaro{\v{s}} and Radek Tyrala},
   title = {GPU-accelerated Evolutionary Design of the
	Complete Exchange Communication on Wormhole
	Networks},
   pages = {1023--1030},
   booktitle = {GECCO '14 Proceedings of the 2014 conference on Genetic and
	evolutionary computation},
   year = {2014},
   location = {New York, NY, US},
   publisher = {Association for Computing Machinery},
   ISBN = {978-1-4503-2662-9},
   doi = {10.1145/2576768.2598315},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=10523}
}

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