title: High Performance Data Clustering: A Comparative Analysis of Performance for GPU, RASC, MPI, and OpenMP Implementations.
abstract: 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.
authors.affiliation: Department of Biological Sciences, Idaho State University.
authors.forename: Luobin
authors.initials: L
authors.lastname: Yang
authors.affiliation: Department of Electrical Engineering and Computer Science, Idaho State University.
authors.forename: Steve C
authors.initials: SC
authors.lastname: Chiu
authors.affiliation: Department of Electrical Engineering and Computer Science, Northwestern University.
authors.forename: Wei-Keng
authors.initials: WK
authors.lastname: Liao
authors.affiliation: Department of Biological Sciences, Idaho State University.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Thomas
keywords: HPC; K-means Clustering; Parallel Data Clustering; Reconfigurable Computing; Scalability