Portal de Periódicos da CAPES

High performance data clustering: a comparative analysis of performance for GPU, RASC, MPI, and OpenMP implementations

2013; Springer Science+Business Media; Volume: 70; Issue: 1 Linguagem: Inglês

10.1007/s11227-013-0906-y

1573-0484

Luobin Yang, Steve C. Chiu, Wei‐keng Liao, Michael A. Thomas,

Data Mining Algorithms and Applications

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.