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

Luobin Yang*, Steve C. Chiu, Wei Keng Liao, Michael A. Thomas

*Corresponding author for this work

Research output: Contribution to journalArticle

11 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)284-300
Number of pages17
JournalJournal of Supercomputing
Volume70
Issue number1
DOIs
StatePublished - Oct 1 2014

Keywords

  • HPC
  • K-means clustering
  • Parallel data clustering
  • Reconfigurable computing
  • Scalability

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture

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