2171

GPU Acceleration of Iterative Clustering

Jesse D. Hall, John C. Hart
University of Illinois at Urbana-Champaign
The ACM Workshop on General Purpose Computing on Graphics Processors, 2004
BibTeX

Download Download (PDF)   View View   Source Source   

1610

views

Iterative clustering algorithms based on Lloyds algorithm (often referred to as the k-means algorithm) have been used in a wide variety of areas, including graphics, computer vision, signal processing, compression, and computational geometry. We describe a method for accelerating many variants of iterative clustering by using programmable graphics hardware to perform the most computationally expensive portion of the work. In particular, we demonstrate significant speedups for k-means clustering (essential in vector quantization) and clustered principal component analysis. An additional contribution is a new hierarchical algorithm for k-means which performs less work than the brute-force algorithm, but which offers significantly more SIMD parallelism than the straightforward hierarchical approach. 1.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2025 hgpu.org

All rights belong to the respective authors

Contact us:

contact@hpgu.org