An Approach for Fast Hierarchical Agglomerative Clustering Using Graphics Processors with CUDA Authors: S.A. Arul Shalom, Manoranjan Dash, Minh Tue Keywords: CUDA. Hierarchical clustering, High performance Computing, Computations using Graphics hardware, complete linkage
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.
GPUs offer a great deal of processing power and programming them may provoke deeper insights into subject identification and mapping.
Topic mappers may be able to claim NVIDIA based software/hardware and/or Sony Playstation 3 and 4 units (Cell Broadband Engine) as a business expense (check with your tax advisor).
A GPU based paper for TMRA 2011 anyone?