High performance data clustering: a comparative analysis of performance for GPU, RASC, MPI, and OpenMP implementations
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Compared to Beowulf clusters and shared-memory machines, GPU and FPGA are emerging alternative architectures that provide massive parallelism and great computational capabilities. These architectures can be utilized to run compute-intensive algorithms to analyze ever-enlarging datasets and provide scalability.
In this paper, we present four implementations of K-means data clustering algorithm for different high performance computing platforms. These four implementations include a CUDA implementation for GPUs, a Mitrion C implementation for FPGAs, an MPI implementation for Beowulf compute clusters, and an OpenMP implementation for shared-memory machines. The comparative analyses of the cost of each platform, difficulty level of programming for each platform, and the performance of each implementation are presented.
KeywordsParallel data clustering K-means clustering Scalability Reconfigurable computing HPC
The authors would like to acknowledge the use of the SGI Altix 4700 located at Idaho National Laboratory for the work performed in this paper, and consultation with Dr. Charles Tolle for the data analysis of this project. The work is part of INL Subcontract/ISU No. 125-229-59.
This work was also made possible by NIH Grant #P20 RR016454 from the INBRE Program of the National Center for Research Resources.
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