Prediction of Optimal Solvers for Sparse Linear Systems Using Deep Learning

TitlePrediction of Optimal Solvers for Sparse Linear Systems Using Deep Learning
Publication TypeConference Proceedings
Year of Publication2022
AuthorsFunk, Y., M. Götz, and H. Anzt
EditorLi, X. S., and K. Teranishi
Conference Name2022 SIAM Conference on Parallel Processing for Scientific Computing (PP)
Pagination14 - 24
Date Published2022
PublisherSociety for Industrial and Applied Mathematics
Conference LocationPhiladelphia, PA

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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