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dc.contributor.advisorMahapatra, Rabi N
dc.creatorTripathy, Aalap
dc.date.accessioned2014-05-13T17:27:19Z
dc.date.available2015-12-01T06:31:21Z
dc.date.created2013-12
dc.date.issued2013-12-06
dc.date.submittedDecember 2013
dc.identifier.urihttp://hdl.handle.net/1969.1/151862
dc.description.abstractThe increasing amount of information accessible to a user digitally makes search difficult, time consuming and unsatisfactory. This has led to the development of active information filtering (recommendation) systems that learn a user’s preference and filter out the most relevant information using sophisticated machine learning techniques. To be scalable and effective, such systems are currently deployed in cloud infrastructures consisting of general-purpose computers. The emergence of many-core processors as compute nodes in cloud infrastructures necessitates a revisit of the computational model, run-time, memory hierarchy and I/O pipelines to fully exploit available concurrency within these processors. This research proposes algorithms & architectures to enhance the performance of content-based (CB) and collaborative information filtering (CF) on many-core processors. To validate these methods, we use Nvidia’s Tesla, Fermi and Kepler GPUs and Intel’s experimental single chip cloud computer (SCC) as the target platforms. We observe that ~290x speedup and up to 97% energy savings over conventional sequential approaches. Finally, we propose and validate a novel reconfigurable SoC architecture which combines the best features of GPUs & SCC. This has been validated to show ~98K speedup over SCC and ~15K speedup over GPU.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectGPGPUen
dc.subjectSCCen
dc.subjectMapreduceen
dc.subjectsemantic information filteringen
dc.subjectcollaborative information filteringen
dc.subjectrecommendation systemen
dc.subjectmany-core computingen
dc.subjectmany-core programming modelsen
dc.subjectreconfigurable architecturesen
dc.subjectSystem on chip (SoC)en
dc.titleHigh Performance Information Filtering on Many-core Processors
dc.typeThesisen
thesis.degree.departmentComputer Science and Engineeringen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorTexas A & M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.levelDoctoralen
dc.contributor.committeeMemberChoi, Gwan S
dc.contributor.committeeMemberChoe, Yoonsuck
dc.contributor.committeeMemberCaverlee, James
dc.type.materialtexten
dc.date.updated2014-05-13T17:27:19Z
local.embargo.terms2015-12-01


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