Dense affinity propagation on clusters of GPUs

Authors:
Marcin Kurdziel;Krzysztof Boryczko
Affiliations:
Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of Computer Science, AGH University of Science and Technology, Krakow, Poland;Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of Computer Science, AGH University of Science and Technology, Krakow, Poland
Venue:
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
Year:
2011

Citing 8
Cited 0

A constant-factor approximation algorithm for the k-median problem

Journal of Computer and System Sciences - STOC 1999
Hierarchical parallel processing of large scale data clustering on a PC cluster with GPU co-processing

The Journal of Supercomputing
Hierarchical clustering of gene expression profiles with graphics hardware acceleration

Pattern Recognition Letters
Efficient K-Means Clustering Using Accelerated Graphics Processors

DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Clustering billions of data points using GPUs

Proceedings of the combined workshops on UnConventional high performance computing workshop plus memory access workshop
A translation system for enabling data mining applications on GPUs

Proceedings of the 23rd international conference on Supercomputing
Efficient band approximation of Gram matrices for large scale kernel methods on GPUs

Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Scalable clustering using graphics processors

WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article focuses on implementation of Affinity Propagation, a state of the art method for finding exemplars in sets of patterns, on clusters of Graphical Processing Units. When finding exemplars in dense, non-metric data Affinity Propagation has O(n2) memory complexity. This limits the size of problems that can fit in the Graphical Processing Unit memory. We show, however, that dense Affinity Propagation can be distributed on multiple Graphical Processing Units with low communication-to-computation ratio. By exploiting this favorable communication pattern we propose an implementation which can find exemplars in large, dense data sets efficiently, even when run over slow interconnect.