Batched Gauss-Jordan Elimination for Block-Jacobi Preconditioner Generation on GPUs
Hartwig Anzt, Jack J. Dongarra, Goran Flegar, Enrique S. Quintana–Ort́ı · 2017
In this paper, we design and evaluate a routine for the efficient generation of block-Jacobi preconditioners on graphics processing units (GPUs). Concretely, to exploit the architecture of the graphics accelerator, we develop a batched Gauss-Jordan elimination CUDA kernel for matrix inversion that embeds an implicit pivoting technique and handles the entire inversion process in the GPU registers. In addition, we integrate extraction and insertion CUDA kernels to rapidly set up the block-Jacobi preconditioner.