Algorithmic transformations in the implementation of K- means clustering on reconfigurable hardware
FPGA '01 Proceedings of the 2001 ACM/SIGDA ninth international symposium on Field programmable gate arrays
Experience with a Hybrid Processor: K-Means Clustering
The Journal of Supercomputing
Efficient K-Means Clustering Using Accelerated Graphics Processors
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
High Performance Data Mining Using R on Heterogeneous Platforms
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
Highly Parameterized K-means Clustering on FPGAs: Comparative Results with GPPs and GPUs
RECONFIG '11 Proceedings of the 2011 International Conference on Reconfigurable Computing and FPGAs
RECONFIG '11 Proceedings of the 2011 International Conference on Reconfigurable Computing and FPGAs
High performance biological pairwise sequence alignment: FPGA versus GPU versus cell BE versus GPP
International Journal of Reconfigurable Computing - Special issue on High-Performance Reconfigurable Computing
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K-means clustering has been widely used in processing large datasets in many fields of studies. Advancement in many data collection techniques has been generating enormous amounts of data, leaving scientists with the challenging task of processing them. Using General Purpose Processors (GPPs) to process large datasets may take a long time; therefore many acceleration methods have been proposed in the literature to speed up the processing of such large datasets. In this work, a parameterized implementation of the K-means clustering algorithm in Field Programmable Gate Array (FPGA) is presented and compared with previous FPGA implementation as well as recent implementations on Graphics Processing Units (GPUs) and GPPs. The proposed FPGA has higher performance in terms of speedup over previous GPP and GPU implementations (two orders and one order of magnitude, resp.). In addition, the FPGA implementation is more energy efficient than GPP and GPU (615x and 31x, resp.). Furthermore, three novel implementations of the K-means clustering based on dynamic partial reconfiguration (DPR) are presented offering high degree of flexibility to dynamically reconfigure the FPGA. The DPR implementations achieved speedups in reconfiguration time between 4x to 15x.