Abstract
A generalized eigensystem problem is usually transformed, utilizing Cholesky decomposition, to a standard eigenproblem. The latter is then solved efficiently by a matrix reduction approach based on Householder tridiagonalization method. We present parallel implementation of an integrated transformation-reduction algorithm on GPU accelerator using CUBLAS. Experimental results clearly demonstrate the potential of data-parallel coprocessors for scientific computations. When comparing against the CPU implementation, the GPU implementations achieve above 16-fold and 26-fold speedups in double precision for reduction and transformation respectively.
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© 2012 Springer-Verlag Berlin Heidelberg
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Zhao, Y., Zhang, J., Chi, X. (2012). Implementations of Main Algorithms for Generalized Eigenproblem on GPU Accelerator. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31020-1_56
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DOI: https://doi.org/10.1007/978-3-642-31020-1_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31019-5
Online ISBN: 978-3-642-31020-1
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