@inproceedings {,
	title = {Prediction of Optimal Solvers for Sparse Linear Systems Using Deep Learning},
	journal = {2022 SIAM Conference on Parallel Processing for Scientific Computing (PP)},
	year = {2022},
	month = {2022},
	pages = {14 - 24},
	publisher = {Society for Industrial and Applied Mathematics},
	address = {Philadelphia, PA},
	abstract = {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{\textquoteright} 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\%.},
	doi = {10.1137/1.978161197714110.1137/1.9781611977141.2},
	url = {https://epubs.siam.org/doi/10.1137/1.9781611977141.2},
	author = {Funk, Yannick and G{\"o}tz, Markus and Anzt, Hartwig},
	editor = {Li, Xiaoye S. and Teranishi, Keita}
}
