High Performance Data Clustering : A Comparative Analysis of Performance for GPU, RASC, MPI, and OpenMP Implementations
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 s...
| Publié dans: | The Journal of supercomputing. - 1998. - 70(2014), 1 vom: 01. Okt., Seite 284-300 |
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| Auteur principal: | |
| Autres auteurs: | , , |
| Format: | Article |
| Langue: | English |
| Publié: |
2014
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| Accès à la collection: | The Journal of supercomputing |
| Sujets: | Journal Article HPC K-means Clustering Parallel Data Clustering Reconfigurable Computing Scalability |
| Résumé: | 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 |
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| Description: | Date Revised 21.10.2021 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 0920-8542 |