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Novel GPU Implementation of Jacobi Algorithm for Karhunen-Loeve Transform of Dense Matrices

Mustafa U. Torun, Onur Yilmaz, Ali N. Akansu
New Jersey Institute of Technology, Department of Electrical and Computer Engineering, University Heights, Newark, NJ 07102, USA
IEEE 46th Annual Conference on Information Sciences and Systems (CISS), 2012

@article{torun6novel,

   title={Novel GPU Implementation of Jacobi Algorithm for Karhunen-Lo{‘e}ve Transform of Dense Matrices},

   author={Torun, M.U. and Yilmaz, O. and Akansu, A.N.},

   journal={parallel computing},

   volume={6},

   pages={7}

}

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Jacobi algorithm for Karhunen-Loeve transform of a symmetric real matrix, and its parallel implementation using chess tournament algorithm are revisited in this paper. Impact of memory access patterns and significance of memory coalescing on the performance of the GPU implementation for the parallel Jacobi algorithm are emphasized. Two novel memory access methods for the Jacobi algorithm are proposed. It is shown with simulation results that one of the proposed methods achieves 77.3% computational performance improvement over the traditional GPU methods, and it runs 73.5 times faster than CPU for a dense symmetric square matrix of size 1,024.
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