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2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (2011)
Anchorage, Alaska USA
May 16, 2011 to May 20, 2011
ISSN: 1530-2075
ISBN: 978-0-7695-4577-6
pp: 1340-1348
ABSTRACT
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.
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CITATION
Grey Ballard, Todd Plantenga, Tamara Kolda, "Efficiently Computing Tensor Eigenvalues on a GPU", 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, vol. 00, no. , pp. 1340-1348, 2011, doi:10.1109/IPDPS.2011.287
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