By Topic

Scalable Triadic Analysis of Large-Scale Graphs: Multi-core vs. Multi-processor vs. Multi-threaded Shared Memory Architectures

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
$31 $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

4 Author(s)
Chin, G. ; Pacific Northwest Nat. Lab., CA, USA ; Marquez, A. ; Choudhury, S. ; Feo, J.

Triadic analysis encompasses a useful set of graph mining methods that are centered on the concept of a triad, which is a sub graph of three nodes. Such methods are often applied in the social sciences as well as many other diverse fields. Triadic methods commonly operate on a triad census that counts the number of triads of every possible edge configuration in a graph. Like other graph algorithms, triadic census algorithms do not scale well when graphs reach tens of millions to billions of nodes. To enable the triadic analysis of large-scale graphs, we developed and optimized a triad census algorithm to efficiently execute on shared memory architectures. We then conducted performance evaluations of the parallel triad census algorithm on three specific systems: CrayXMT, HP Superdome, and AMD multi-core NUMA machine. These three systems have shared memory architectures but with markedly different hardware capabilities to manage parallelism.

Published in:

Computer Architecture and High Performance Computing (SBAC-PAD), 2012 IEEE 24th International Symposium on

Date of Conference:

24-26 Oct. 2012