Title :
Speedup of Fuzzy Clustering Through Stream Processing on Graphics Processing Units
Author :
Anderson, Derek T. ; Luke, Robert H. ; Keller, James M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Missouri, Columbia, MO
Abstract :
As the number of data points, feature dimensionality, and number of centers for clustering algorithms increase, computational tractability becomes a problem. The fuzzy c-means has a large degree of inherent algorithmic parallelism that modern CPU architectures do not exploit. Many pattern recognition algorithms can be sped up on a graphics processing unit (GPU) as long as the majority of computation at various stages and the components are not dependent on each other. We present a generalized method for offloading fuzzy clustering to a GPU, while maintaining control over the number of data points, feature dimensionality, and the number of cluster centers. GPU-based clustering is a high-performance low-cost solution that frees up the CPU. Our results show a speed increase of over two orders of magnitude for particular clustering configurations and platforms.
Keywords :
coprocessors; fuzzy set theory; pattern clustering; CPU architectures; computational tractability; fuzzy c-means; fuzzy clustering; graphics processing units; stream processing; Fuzzy C-Means; Fuzzy Clustering; Fuzzy clustering; Graphics Processing Units; Stream Processing; fuzzy c-means; graphics processing units (GPUs); stream processing;
Journal_Title :
Fuzzy Systems, IEEE Transactions on
DOI :
10.1109/TFUZZ.2008.924203