By Topic

Data Parallelism for Belief Propagation in Factor Graphs

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Nam Ma ; Comput. Sci. Dept., Univ. of Southern California, Los Angeles, CA, USA ; Yinglong Xia ; Prasanna, V.K.

We investigate data parallelism for belief propagation in a cyclic factor graphs on multicore/many core processors. Belief propagation is a key problem in exploring factor graphs, a probabilistic graphical model that has found applications in many domains. In this paper, we identify basic operations called node level primitives for updating the distribution tables in a factor graph. We develop algorithms for these primitives to explore data parallelism. We also propose a complete belief propagation algorithm to perform exact inference in such graphs. We implement the proposed algorithms on state-of-the-art multicore processors and show that the proposed algorithms exhibit good scalability using a representative set of factor graphs. On a 32-core Intel Nehalem-EX based system, we achieve 30× speedup for the primitives and 29× for the complete algorithm using factor graphs with large distribution tables.

Published in:

Computer Architecture and High Performance Computing (SBAC-PAD), 2011 23rd International Symposium on

Date of Conference:

26-29 Oct. 2011