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|Title:||An approach for fast Hierarchical agglomerative clustering using graphics processors with CUDA||Authors:||Shalom, S.A.A.
Computations using graphics hardware
High performance computing
|Issue Date:||2010||Citation:||Shalom, S.A.A.,Dash, M.,Tue, M. (2010). An approach for fast Hierarchical agglomerative clustering using graphics processors with CUDA. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6119 LNAI (PART 2) : 35-42. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-13672-6_4||Abstract:||Graphics Processing Units in today's desktops can well be thought of as a high performance parallel processor. Each single processor within the GPU is able to execute different tasks independently but concurrently. Such computational capabilities of the GPU are being exploited in the domain of Data mining. Two types of Hierarchical clustering algorithms are realized on GPU using CUDA. Speed gains from 15 times up to about 90 times have been realized. The challenges involved in invoking Graphical hardware for such Data mining algorithms and effects of CUDA blocks are discussed. It is interesting to note that block size of 8 is optimal for GPU with 128 internal processors. © 2010 Springer-Verlag Berlin Heidelberg.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/128557||ISBN:||3642136710||ISSN:||03029743||DOI:||10.1007/978-3-642-13672-6_4|
|Appears in Collections:||Staff Publications|
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