IEEE Transactions on Power Systems · 2017 · 27 citations · 3 references
ComputingComputer ScienceHigh-performance ComputingParallel AlgorithmsParallel ComputingScientific ComputingData ParallelismGraphics Processing Unit ComputingMatrix ComputationParallel Numerical Linear AlgebraParallel Numerical MethodsParallel Performance AnalysisGpu Parallel AlgorithmsMonte Carlo Power SimulationParallel Flow AlgorithmsParallel SolversParallel Sparse Linear AlgebraPower Flow AnalysisScientific Computing On GpusStatic Security
In many power system applications, such as N-x static security analysis and Monte-Carlo-simulation-based probabilistic power flow (PF) analysis, it is a very time-consuming task to analyze massive number of PFs on identical or similar network topology. This letter presents a novel GPU-accelerated batch LU-factorization solver that achieves higher level of parallelism and better memory-access efficiency through packaging massive number of LU-factorization tasks to formulate a new larger-scale problem. The proposed solver can achieve up to 76 times speedup when compared to KLU library and lays a critical foundation for massive-PFs-solving applications.
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