Efficient Two-Level Preconditioned Conjugate Gradient Method on the GPU

摘要

We present an implementation of a Two-Level Preconditioned Conjugate Gradient Method for the GPU. We investigate a Truncated Neumann Series based preconditioner in combination with deflation. This combination exhibits fine-grain parallelism and hence we gain considerably in execution time when compared with a similar implementation on the CPU. Its numerical performance is comparable to the Block Incomplete Cholesky approach. Our method provides a speedup of up to 16 for a system of one million unknowns when compared to an optimized implementation on one core of the CPU.