GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems
SIAM Journal on Scientific and Statistical Computing
Proceedings of the 37th Annual Design Automation Conference
Extended Krylov subspace method for reduced order analysis of linear circuits with multiple sources
Proceedings of the 37th Annual Design Automation Conference
Efficient large-scale power grid analysis based on preconditioned krylov-subspace iterative methods
Proceedings of the 38th annual Design Automation Conference
Random walks in a supply network
Proceedings of the 40th annual Design Automation Conference
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
3D-ICE: fast compact transient thermal modeling for 3D ICs with inter-tier liquid cooling
Proceedings of the International Conference on Computer-Aided Design
Sparse LU factorization for parallel circuit simulation on GPU
Proceedings of the 49th Annual Design Automation Conference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the International Conference on Computer-Aided Design
Deterministic random walk preconditioning for power grid analysis
Proceedings of the International Conference on Computer-Aided Design
Efficient parallel power grid analysis via additive Schwarz method
Proceedings of the International Conference on Computer-Aided Design
Circuit simulation via matrix exponential method for stiffness handling and parallel processing
Proceedings of the International Conference on Computer-Aided Design
GPU-accelerated preconditioned iterative linear solvers
The Journal of Supercomputing
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In this paper, we propose an efficient parallel dynamic linear solver, called GPU-GMRES, for transient analysis of large power grid networks. The new method is based on the preconditioned generalized minimum residual (GMRES) iterative method implemented on heterogeneous CPU-GPU platforms. The new solver is very robust and can be applied to power grids with different structures and other applications like thermal analysis. The proposed GPU-GMRES solver adopts the very general and robust incomplete LU (ILU) based preconditioner. We show that by properly selecting the right amount of fill-ins in the incomplete LU factors, a good trade-off between GPU efficiency and GMRES convergence rate can be achieved for the best overall performance. Such a tunable feature makes this algorithm very adaptive to different problems. Furthermore, we properly partition the major computing tasks in GMRES solver to minimize the data traffic between CPU and GPU, which further boosts performance of the proposed method. Experimental results on the set of published IBM benchmark circuits and mesh-structured power grid networks show that the GPU-GMRES solver can deliver order of magnitudes speedup over the direct LU solver UMFPACK. GPU-GMRES can also deliver 3-10x speedup over the CPU implementation of the same GMRES method on transient analysis.