Block Conjugate-Gradient Method With Multilevel Preconditioning and GPU Acceleration for FEM Problems in Electromagnetics
- 27 April 2018
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Antennas and Wireless Propagation Letters
- Vol. 17 (6) , 1039-1042
Abstract
In this paper a GPU-accelerated block conjugate-gradient solver with multilevel preconditioning is presented for solving large system of sparse equations with multiple right-hand-sides (RHSs) which arise in the finite-element analysis of electromagnetic problems. We demonstrate that blocking reduces the time to solution significantly and allows for better utilization of the computing power of GPUs, especially when the system matrix is complex-valued. We show that, for a complex-valued sparse matrix with over 1.2 million unknowns and sixteen right-hand-sides, the block version of the preconditioned conjugate-gradient method using a single NVIDIA Tesla P100 accelerator is almost three times faster than a nonblocked version. Numerical tests have also shown that a CPU-only nonblocked complex implementation is unlikely to benefit much from blocking. Compared to an optimized CPU-only solver using an Intel Xeon E5-2680 v3 with twelve cores, the nonblocked GPU-accelerated version was 4.5 times faster, while the blocked version was 12.4 times faster.Keywords
Funding Information
- Polish National Science Centre (DEC-2014/13/B/ST7/01173)
- Foundation For Polish Science
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