%0 Conference Paper %B International Conference on Computational Science (ICCS 2017) %D 2017 %T Optimizing the SVD Bidiagonalization Process for a Batch of Small Matrices %A Tingxing Dong %A Azzam Haidar %A Stanimire Tomov %A Jack Dongarra %X A challenging class of problems arising in many GPU applications, called batched problems, involves linear algebra operations on many small-sized matrices. We designed batched BLAS (Basic Linear Algebra Subroutines) routines, and in particular the Level-2 BLAS GEMV and the Level-3 BLAS GEMM routines, to solve them. We proposed device functions and big-tile settings in our batched BLAS design. We adopted auto-tuning to optimize different instances of GEMV routines. We illustrated our batched BLAS approach to optimize batched bi-diagonalization progressively on a K40c GPU. The optimization techniques in this paper are applicable to the other two-sided factorizations as well. %B International Conference on Computational Science (ICCS 2017) %I Procedia Computer Science %C Zurich, Switzerland %8 2017-06 %G eng %U http://www.sciencedirect.com/science/article/pii/S1877050917308645 %R https://doi.org/10.1016/j.procs.2017.05.237