Efficient large-scale power grid analysis based on preconditioned krylov-subspace iterative methods
Proceedings of the 38th annual Design Automation Conference
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Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
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HotPar'11 Proceedings of the 3rd USENIX conference on Hot topic in parallelism
Fast Poisson solver preconditioned method for robust power grid analysis
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Proceedings of the International Conference on Computer-Aided Design
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Proceedings of the International Conference on Computer-Aided Design
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Proceedings of the Conference on Design, Automation and Test in Europe
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Proceedings of the 50th Annual Design Automation Conference
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Leveraging the power of nowadays graphics processing units for robust power grid simulation remains a challenging task. Existing preconditioned iterative methods that require incomplete matrix factorizations can not be effectively accelerated on GPU due to its limited hardware resource as well as data parallel computing. This work presents an efficient GPU-based multigrid preconditioning algorithm for robust power grid analysis. An ELL-like sparse matrix data structure is adopted and implemented specifically for power grid analysis to assure coalesced GPU device memory access and high arithmetic intensity. By combining the fast geometrical multigrid solver with the robust Krylov-subspace iterative solver, power grid DC and transient analysis can be performed efficiently on GPU without loss of accuracy (largest errors X speedups over the best available CPU-based solvers. An industrial power grid with 10.5 million nodes can be accurately solved in 12 seconds.