symPACK: A GPU-Capable Fan-Out Sparse Cholesky Solver
Julian Bellavita, Mathias Jacquelin, Esmond Ng, Dan Bonachea, Johnny Corbino, Paul Hargrove · 2023
Sparse symmetric positive definite systems of equations are ubiquitous in scientific workloads and applications. Parallel sparse Cholesky factorization is the method of choice for solving such linear systems. Therefore, the development of parallel sparse Cholesky codes that can efficiently run on today’s large-scale heterogeneous distributed-memory platforms is of vital importance. Modern supercomputers offer nodes that contain a mix of CPUs and GPUs. To fully utilize the computing power of these nodes, scientific codes must be adapted to offload expensive computations to GPUs.