Cholesky and Gram-Schmidt Orthogonalization for Tall-and-Skinny QR Factorizations on Graphics Processors
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We present a method for the QR factorization of large tall-and-skinny matrices that combines block Gram-Schmidt and the Cholesky decomposition to factorize the input matrix column panels, overcoming the sequential nature of this operation. This method uses re-orthogonalization to obtain a satisfactory level of orthogonality both in the Gram-Schmidt process and the Cholesky QR.
Our approach has the additional benefit of enabling the introduction of a static look-ahead technique for computing the Cholesky decomposition on the CPU while the remaining operations (all Level-3 BLAS) are performed on the GPU.
In contrast with other specific factorizations for tall-skinny matrices, the novel method has the key advantage of not requiring any custom GPU kernels. This simplifies the implementation and favours portability to future GPU architectures.
Our experiments show that, for tall-skinny matrices, the new approach outperforms the code in MAGMA by a large margin, while it is very competitive for square matrices when the memory transfers and CPU computations are the bottleneck of Householder QR.
KeywordsQR factorization Tall-and-skinny matrices Graphics processing unit Gram-Schmidt Cholesky factorization Look-ahead High-performance
This research was supported by the project TIN2017-82972-R from the MINECO (Spain), and the EU H2020 project 732631 “OPRECOMP. Open Transprecision Computing”.
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