Visual exploration of data by using multidimensional scaling on multicore CPU, GPU, and MPI cluster

Piotr Pawliczek, Witold Dzwinel, David A. Yuen

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Visual and interactive data exploration requires fast and reliable tools for embedding of an original data space in 3(2)-dimensional Euclidean space. Multidimensional scaling (MDS) is a good candidate. However, owing to at least O(M2) memory and time complexity, MDS is computationally demanding for interactive visualization of data sets consisting of order of 104 objects on computer systems, ranging from PC with multicore CPU processor, graphics processing unit (GPU) board to midrange MPI clusters. To explore interactively data sets of that size, we have developed novel efficient parallel algorithms for MDS mapping based on virtual particle dynamics. We demonstrate that the performance of our MDS algorithms implemented in compute unified device architecture environment on a PC equipped with a modern GPU board (Tesla M2090, GeForce GTX 480) is considerably faster than its MPI/OpenMP parallel implementation on the modern midrange professional cluster (10 nodes, each equipped with 2x Intel Xeon X5670 CPUs). We also show that the hybridized two-level MPI/CUDA implementation, run on a cluster of GPU nodes, can additionally provide a linear speedup. Copyright 2013 John Wiley & Sons, Ltd.

Original languageEnglish (US)
Pages (from-to)662-682
Number of pages21
JournalConcurrency Computation Practice and Experience
Volume26
Issue number3
DOIs
StatePublished - Mar 10 2014

Keywords

  • GPU-CUDA
  • MPI cluster
  • data mining
  • interactive data visualization
  • method of particles
  • multicore CPU
  • multidimensional scaling

Fingerprint Dive into the research topics of 'Visual exploration of data by using multidimensional scaling on multicore CPU, GPU, and MPI cluster'. Together they form a unique fingerprint.

Cite this