KFusion: obtaining modularity and performance with regards to general purpose GPU computing and co-processors

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dc.contributor.authorKiemele, Liam
dc.contributor.supervisorCoady, Yvonne
dc.date.accessioned2012-12-14T21:13:06Z
dc.date.available2012-12-14T21:13:06Z
dc.date.copyright2012en_US
dc.date.issued2012-12-14
dc.degree.departmentDepartment of Computer Science
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractConcurrency has recently come to the forefront of computing as multi-core processors become more and more common. General purpose graphics processing unit computing brings with them new language support for dealing with co-processor environments such as OpenCL and CUDA. Programming language support for multi-core architectures introduces a fundamentally new mechanism for modularity--a kernel. Developers attempting to leverage these mechanism to separate concerns often incur unanticipated performance penalties. My proposed solution aims to preserve the benefits of kernel boundaries for modularity, while at the same time eliminate these inherent costs at compile time and execution. KFusion is a prototype tool for transforming programs written in OpenCL to make them more efficient. By leveraging loop fusion and deforestation, it can eliminate the costs associated with compositions of kernels that share data. Case studies show that Kfusion can address key memory bandwidth and latency bottlenecks and result in substantial performance improvements.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/4357
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.subjectmodularityen_US
dc.subjectperformanceen_US
dc.subjectOpenCLen_US
dc.subjectconcurrencyen_US
dc.subjectparallelismen_US
dc.titleKFusion: obtaining modularity and performance with regards to general purpose GPU computing and co-processorsen_US
dc.typeThesisen_US

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