A Parallel Implementation of the Gustafson-Kessel Clustering Algorithm with CUDA

• Published in: IEICE Transactions on Information and Systems Vol. E95.D; no. 4; pp. 1162 - 1165
• Main Authors: SEO, Jeong Bong, KIM, Dae-Won
• Format: Journal Article
• Language: English
• Published: Oxford The Institute of Electronics, Information and Communication Engineers 2012; Oxford University Press
• Subjects: Applied sciences; clustering; Computer science; control theory; systems; CUDA; Design. Technologies. Operation analysis. Testing; Electronics; Exact sciences and technology; GPU; Gustafson-Kessel; Information, signal and communications theory; Integrated circuits; Integrated circuits by function (including memories and processors); Memory and file management (including protection and security); Memory organisation. Data processing; Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices; Signal and communications theory; Signal representation. Spectral analysis; Signal, noise; Software; Telecommunications and information theory; Automatic classification; Multiplication; Processor; Resource sharing; Algorithm; Signal classification; GPU; Implementation; Optimization; Aggregation; CUDA; Integrated circuit; clustering; Shared memory; Gustafson-Kessel
• ISSN: 0916-8532; 1745-1361
• DOI: 10.1587/transinf.E95.D.1162

Despite the benefits of the Gustafson-Kessel (GK) clustering algorithm, it becomes computationally inefficient when applied to high-dimensional data. In this letter, a parallel implementation of the GK algorithm on the GPU with CUDA is proposed. Using an optimized matrix multiplication algorithm with fast access to shared memory, the CUDA version achieved a maximum 240-fold speedup over the single-CPU version.