A Multilevel Subtree Method for Single and Batched Sparse Cholesky Factorization
Meng Tang, Mohamed Gadou, Steven C. Rennich, Timothy A. Davis, Sanjay Ranka · 2018
Scientific computing relies heavily on matrix factorization. Cholesky factorization is typically used to solve the linear equation system Ax = b where A is symmetric and positive definite. A large number of applications require operating on sparse matrices. A major overhead with factorization of sparse matrices on GPUs is addressing the cost of transferring the data from the CPU to the GPU. Additionally, the computational efficiency of factorization of small dense matrices has to be addressed.