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Investigating Linear Solvers for Power Grid Analysis with Exascale Computing: A Journey of Learning and Collaboration
DescriptionThis work is a contribution to the advancement of linear solvers for the Exascale Computing Project. It focuses on direct sparse linear solvers using High-Performance Computing (HPC) for large-scale power systems, resembling the United States power grids. This paper explores supercomputers at Oak Ridge Leadership Computing Facility, Summit and Frontier, comparing both performance and optimization strategies. The project encompasses a comprehensive test bench for Trilinos Amesos2 CPU-based solvers, KLU2 and ShyLUBasker, and the testing of GPU-based solvers from NVIDIA cuSolver to AMD rocSolver on distinct architecture configurations. The challenges of power flow analysis are addressed through optimization techniques, like matrix symmetry and GPU acceleration, and by evaluating accuracy and stability of linear solvers through residual analysis. Beyond technical gains, this work underscores the significance of collaboration and diverse expertise in HPC for innovative analysis of power grid systems, critical for resilient infrastructure against burgeoning threats like climate change and cyberattacks.
