Multi-GPU island-based genetic algorithm for solving the knapsack problem

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dc.contributor.authorJaros, Jiri
dc.coverage.spatialBrisbane Australia
dc.date.accessioned2015-12-10T23:32:46Z
dc.date.createdJune 10-15 2012
dc.date.issued2012
dc.date.updated2016-02-24T08:51:36Z
dc.description.abstractThis paper introduces a novel implementation of the genetic algorithm exploiting a multi-GPU cluster. The proposed implementation employs an island-based genetic algorithm where every GPU evolves a single island. The individuals are processed by CUDA warps, which enables the solution of large knapsack instances and eliminates undesirable thread divergence. The MPI interface is used to exchange genetic material among isolated islands and collect statistical data. The characteristics of the proposed GAs are investigated on a two-node cluster composed of 14 Fermi GPUs and 4 six-core Intel Xeon processors. The overall GPU performance of the proposed GA reaches 5.67 TFLOPS.
dc.identifier.isbn9781467315098
dc.identifier.urihttp://hdl.handle.net/1885/68981
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseriesIEEE Congress on Evolutionary Computation (CEC 2012)
dc.source2012 IEEE Congress on Evolutionary Computation Proceedings
dc.subjectKeywords: CUDA; GPU; Island model; knapsack; MPI; Gallium; Interfaces (materials); Program processors; Genetic algorithms CUDA; GA; GPU; island model; knapsack; MPI
dc.titleMulti-GPU island-based genetic algorithm for solving the knapsack problem
dc.typeConference paper
local.bibliographicCitation.lastpage224
local.bibliographicCitation.startpage217
local.contributor.affiliationJaros, Jiri, College of Engineering and Computer Science, ANU
local.contributor.authoruidJaros, Jiri, u5053499
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080205 - Numerical Computation
local.identifier.ariespublicationf5625xPUB1884
local.identifier.doi10.1109/CEC.2012.6256131
local.identifier.scopusID2-s2.0-84866858943
local.type.statusPublished Version

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