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Approach of automatic selection of parallel sparse linear solver and tuning its parameters is discussed. Performance of linear solver is considered as function of sparse matrix features, selected sparse solver and its settings, hardware platform. Supervised learning is used to create this function and is based on gathering of empirical performance data and the following use of the support vector regression. After creating complexity function, a genetic algorithm is used to find its minimum across the solver types and settings to find locally optimal solver. Results of experiments with PETSc linear solvers are presented for several classes of sparse problems from different applications.
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