Abstract
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. © 2013 Springer-Verlag.
Cite
CITATION STYLE
Gupta, R., Van Gijzen, M. B., & Vuik, C. K. (2013). Efficient two-level preconditioned conjugate gradient method on the GPU. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7851 LNCS, pp. 36–49). https://doi.org/10.1007/978-3-642-38718-0_7
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