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Proceedings of the 2022 SIAM Conference on Parallel Processing for Scientific Computing

Prediction of Optimal Solvers for Sparse Linear Systems Using Deep Learning


Solving sparse linear systems is a key task in a number of computational problems, such as data analysis and simulations, and majorly determines overall execution time. Choosing a suitable iterative solver algorithm, however, can significantly improve time-to-completion. We present a deep learning approach designed to predict the optimal iterative solver for a given sparse linear problem. For this, we detail useful linear system features to drive the prediction process, the metrics we use to quantify the iterative solvers' time-to-approximation performance and a comprehensive experimental evaluation of the prediction quality of the neural network. Using a hyperparameter optimization and an ablation study on the SuiteSparse matrix collection we have inferred the importance of distinct features, achieving a top-1 classification accuracy of 60%.

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Proceedings of the 2022 SIAM Conference on Parallel Processing for Scientific Computing
Pages: 14 - 24
Editors: Xiaoye S. Li, Lawrence Berkeley National Laboratory, Berkeley, California, USA and Keita Teranishi, Sandia National Laboratories, Albuquerque, New Mexico, USA
ISBN (Online): 978-1-61197-714-1


Published online: 7 February 2022



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