Tree structured data processing on GPUs

dc.contributor.advisorBhavsar, Virendrakumar
dc.contributor.authorLu, Yifan
dc.date.accessioned2023-03-01T16:43:19Z
dc.date.available2023-03-01T16:43:19Z
dc.date.issued2014
dc.date.updated2020-07-03T00:00:00Z
dc.description.abstractTree-structured data are used in many applications. In order to reduce the computing time for processing large tree-structured data sets, parallel processing has been used. Recently, research has been done on parallel computing of tree-structured data on graphics processing units (GPUs). However, tree data structures on GPUs are commonly applied to storing a particular kind of tree, and support limited types of tree traversals. In this thesis, we propose a tree data structure which can apply to storing many types of trees, and support four common types of tree traversals: pre-order, postorder, in-order and breadth-first traversals. Therefore, most tree algorithms can be implemented on GPUs by using this data structure. We implemented a weighted similarity algorithm on an NVIDIA GPU for demonstration of the performance of this data structure. The results showed that this GPU application can get speedup of about 4000 compared to an application running on a single AMD Opteron CPU core.
dc.description.copyright© Yifan Lu, 2015
dc.description.noteScanned from archival print submission.
dc.formattext/xml
dc.format.extentxiii, 100 pages
dc.format.mediumelectronic
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/14383
dc.language.isoen_CA
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineComputer Science
dc.titleTree structured data processing on GPUs
dc.typemaster thesis
thesis.degree.disciplineComputer Science
thesis.degree.fullnameMaster of Computer Science
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.C.S.

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