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High performance data clustering: a comparative analysis of performance for GPU, RASC, MPI, and OpenMP implementations
The Journal of Supercomputing ( IF 3.3 ) Pub Date : 2013-03-30 , DOI: 10.1007/s11227-013-0906-y
Luobin Yang 1 , Steve C Chiu 2 , Wei-Keng Liao 3 , Michael A Thomas 1
Affiliation  

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.

中文翻译:

高性能数据聚类:GPU、RASC、MPI 和 OpenMP 实现的性能比较分析

与 Beowulf 集群和共享内存机器相比,GPU 和 FPGA 是新兴的替代架构,可提供大规模并行性和强大的计算能力。这些架构可用于运行计算密集型算法,以分析不断扩大的数据集并提供可扩展性。在本文中,我们介绍了 K-means 数据聚类算法的四种实现,适用于不同的高性能计算平台。这四种实现包括针对 GPU 的 CUDA 实现、针对 FPGA 的 Mitrion C 实现、针对 Beowulf 计算集群的 MPI 实现以及针对共享内存机器的 OpenMP 实现。给出了每个平台的成本、每个平台的编程难度级别以及每个实现的性能的比较分析。
更新日期:2013-03-30
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