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Publication detail
JAROŠ, J. TYRALA, R.
Original Title
GPU-accelerated Evolutionary Design of the Complete Exchange Communication on Wormhole Networks
Type
conference paper
Language
English
Original Abstract
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.
Keywords
Complete exchange communication, Collective communications, communication scheduling, evolutionary design, GPU-based acceleration, multi-GPU systems.
Authors
JAROŠ, J.; TYRALA, R.
RIV year
2014
Released
12. 7. 2014
Publisher
Association for Computing Machinery
Location
New York, NY
ISBN
978-1-4503-2662-9
Book
GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference
Pages from
1023
Pages to
1030
Pages count
8
URL
http://dl.acm.org/citation.cfm?id=2576768.2598315
BibTex
@inproceedings{BUT111530, author="Jiří {Jaroš} and Radek {Tyrala}", title="GPU-accelerated Evolutionary Design of the Complete Exchange Communication on Wormhole Networks", booktitle="GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference", year="2014", pages="1023--1030", publisher="Association for Computing Machinery", address="New York, NY", doi="10.1145/2576768.2598315", isbn="978-1-4503-2662-9", url="http://dl.acm.org/citation.cfm?id=2576768.2598315" }