Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105723
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Matsumoto, T | en_US |
dc.creator | Yiu, ML | en_US |
dc.date.accessioned | 2024-04-15T07:36:14Z | - |
dc.date.available | 2024-04-15T07:36:14Z | - |
dc.identifier.isbn | 978-1-4673-9504-5 (Electronic) | en_US |
dc.identifier.isbn | 978-1-4673-9503-8 (CD) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/105723 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication T. Matsumoto and M. L. Yiu, "Accelerating Exact Similarity Search on CPU-GPU Systems," 2015 IEEE International Conference on Data Mining, Atlantic City, NJ, USA, 2015, pp. 320-329 is available at https://doi.org/10.1109/ICDM.2015.125. | en_US |
dc.title | Accelerating exact similarity search on CPU-GPU systems | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 320 | en_US |
dc.identifier.epage | 329 | en_US |
dc.identifier.doi | 10.1109/ICDM.2015.125 | en_US |
dcterms.abstract | In recent years, the use of Graphics Processing Units (GPUs) for data mining tasks has become popular. With modern processors integrating both CPUs and GPUs, it is also important to consider what tasks benefit from GPU processing and which do not, and apply a heterogeneous processing approach to improve the efficiency where applicable. Similarity search, also known as k-nearest neighbor search, is a key part of data mining applications and is used also extensively in applications such as multimedia search, where only a small subset of possible results are used. Our contribution is a new exact kNN algorithm with a compressed partial heapsort that outperforms other state-of-the-art exact kNN algorithms by leveraging both the GPU and CPU. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | 15th IEEE International Conference on Data Mining, 14-17 November 2015, Atlantic City, New Jersey, p. 320-329 | en_US |
dcterms.issued | 2015 | - |
dc.identifier.scopus | 2-s2.0-84963541579 | - |
dc.relation.conference | IEEE International Conference on Data Mining [ICDM] | - |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-1591 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 9570451 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
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Yiu_Accelerating_Exact_Similarity.pdf | Pre-Published version | 1.31 MB | Adobe PDF | View/Open |
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