Fast 1-itemset frequency count using CUDA
College
College of Computer Studies
Department/Unit
Computer Technology
Document Type
Conference Proceeding
Source Title
IEEE Region 10 Annual International Conference, Proceedings/TENCON
First Page
210
Last Page
213
Publication Date
2-9-2017
Abstract
Frequent itemset mining is one of the main and compute-intensive operations in the field of data mining. The said algorithm is use in finding frequent patterns in transactional databases. The 1-itemset frequent count is used as basis for finding succeeding k-itemset mining. Thus there is a need to speed-up this process. One of the techniques to speed-up the process is using the Single Instruction Multiple Thread (SIMT) architecture. This architecture allows a single instruction to be applied to multiple threads at the same time. Current graphics processing unit (GPU), which contains multiple streaming processing units, uses SIMT architecture. In order to abstract the GPU hardware from the programming model, NVIDIA introduces the compute unified device architecture (CUDA) as an extension to existing programming languages in order to support SIMT. This paper discusses how 1-itemset frequent count is implemented in SIMT using CUDA.
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Digitial Object Identifier (DOI)
10.1109/TENCON.2016.7847991
Recommended Citation
Uy, R., & Marcos, N. (2017). Fast 1-itemset frequency count using CUDA. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 210-213. https://doi.org/10.1109/TENCON.2016.7847991
Disciplines
Computer Sciences
Keywords
Data mining; Big data; CUDA (Computer architecture)
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