The search functionality is under construction.
The search functionality is under construction.

GPGPU Implementation of Variational Bayesian Gaussian Mixture Models

Hiroki NISHIMOTO, Renyuan ZHANG, Yasuhiko NAKASHIMA

  • Full Text Views

    0

  • Cite this

Summary :

The efficient implementation strategy for speeding up high-quality clustering algorithms is developed on the basis of general purpose graphic processing units (GPGPUs) in this work. Among various clustering algorithms, a sophisticated Gaussian mixture model (GMM) by estimating parameters through variational Bayesian (VB) mechanism is conducted due to its superior performances. Since the VB-GMM methodology is computation-hungry, the GPGPU is employed to carry out massive matrix-computations. To efficiently migrate the conventional CPU-oriented schemes of VB-GMM onto GPGPU platforms, an entire migration-flow with thirteen stages is presented in detail. The CPU-GPGPU co-operation scheme, execution re-order, and memory access optimization are proposed for optimizing the GPGPU utilization and maximizing the clustering speed. Five types of real-world applications along with relevant data-sets are introduced for the cross-validation. From the experimental results, the feasibility of implementing VB-GMM algorithm by GPGPU is verified with practical benefits. The proposed GPGPU migration achieves 192x speedup in maximum. Furthermore, it succeeded in identifying the proper number of clusters, which is hardly conducted by the EM-algotihm.

Publication
IEICE TRANSACTIONS on Information Vol.E105-D No.3 pp.611-622
Publication Date
2022/03/01
Publicized
2021/11/24
Online ISSN
1745-1361
DOI
10.1587/transinf.2021EDP7121
Type of Manuscript
PAPER
Category
Fundamentals of Information Systems

Authors

Hiroki NISHIMOTO
  Nara Institute of Science and Technology
Renyuan ZHANG
  Nara Institute of Science and Technology
Yasuhiko NAKASHIMA
  Nara Institute of Science and Technology

Keyword