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(n 2) 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. © 2012 Springer-Verlag.
CITATION STYLE
Kurdziel, M., & Boryczko, K. (2012). Dense affinity propagation on clusters of GPUs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7203 LNCS, pp. 599–608). https://doi.org/10.1007/978-3-642-31464-3_61
Mendeley helps you to discover research relevant for your work.