Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Efficiently Computing Tensor Eigenvalues on a GPU

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

3 Author(s)
Ballard, G. ; Comput. Sci. Dept., UC Berkeley, Berkeley, CA, USA ; Kolda, T. ; Plantenga, T.

The tensor eigenproblem has many important applications, generating both mathematical and application-specific interest in the properties of tensor eigenpairs and methods for computing them. A tensor is an m-way array, generalizing the concept of a matrix (a 2-way array). Kolda and Mayo have recently introduced a generalization of the matrix power method for computing real-valued tensor eigenpairs of symmetric tensors. In this work, we present an efficient implementation of their algorithm, exploiting symmetry in order to save storage, data movement, and computation. For an application involving repeatedly solving the tensor eigenproblem for many small tensors, we describe how a GPU can be used to accelerate the computations. On an NVIDIA Tesla C 2050 (Fermi) GPU, we achieve 318 Gflops/s (31% of theoretical peak performance in single precision) on our test data set.

Published in:

Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on

Date of Conference:

16-20 May 2011