Factorized Sparse Approximate Inverses on GPUs

GPUs exhibit significantly higher peak performance than conventional CPUs. However, due to their programming model and microarchitecture, only highly parallel algorithms can exploit their full potential. In this context, the FSAI preconditioner may represent – with its inherent parallelism – an optimal candidate for the conjugate gradient-like solution of sparse linear systems. While its application to a vector involves only matrix-vector products, a GPU-based implementation requires a nontrivial recasting of multiple computational steps.