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Takagi Factorization on GPU using CUDA

Gagandeep S. Sachdev, Vishay Vanjani and Mary W. Hall
School of Computing, University of Utah, UT, 84102
Symposium on Application Accelerators in High Performance Computing, 2010

@article{sachdev2010takagi,

   title={Takagi Factorization on GPU using CUDA},

   author={Sachdev, G.S. and Vanjani, V. and Hall, M.W.},

   booktitle={Application Accelerators in High Performance Computing, 2010 Symposium, Papers},

   year={2010}

}

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Takagi factorization or symmetric singular value decomposition is a special form of SVD applicable to symmetric complex matrices. The computation takes advantage of symmetry to reduce computation and storage requirements. The Jacobi method with chess tournament ordering was used to perform the computation in parallel on a GPU using the CUDA programming model. We were able to achieve speedups of over 11x and 7x over CPU serial and Pthreads implementations, respectively, for matrix sizes greater than 512×512.
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