@inproceedings {,
	title = {{Integrating process, control-flow, and data resiliency layers using a hybrid Fenix/Kokkos approach}},
	journal = {2022 IEEE International Conference on Cluster Computing (CLUSTER 2022)},
	year = {2022},
	month = {2022-09},
	address = {Heidelberg, Germany},
	keywords = {checkpointing, Fault tolerance, Fenix, HPC, Kokkos, MPI-ULFM, resilience},
	url = {https://hal.archives-ouvertes.fr/hal-03772536},
	author = {Whitlock, Matthew and Morales, Nicolas and George Bosilca and Bouteiller, Aur{\'e}lien and Nicolae, Bogdan and Teranishi, Keita and Giem, Elisabeth and Sarkar, Vivek}
}
@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}
}
@article {,
	title = {Resiliency in numerical algorithm design for extreme scale simulations},
	journal = {The International Journal of High Performance Computing Applications},
	volume = {36371337212766180823},
	year = {2022},
	month = {2022-03},
	pages = {251 - 285},
	keywords = {Fault tolerance, Numerical algorithms, parallel computer architecture, resilience},
	issn = {1094-3420},
	doi = {10.1177/10943420211055188},
	url = {http://journals.sagepub.com/doi/10.1177/10943420211055188http://journals.sagepub.com/doi/pdf/10.1177/10943420211055188http://journals.sagepub.com/doi/pdf/10.1177/10943420211055188http://journals.sagepub.com/doi/full-xml/10.1177/10943420211055188},
	author = {Agullo, Emmanuel and Altenbernd, Mirco and Anzt, Hartwig and Bautista-Gomez, Leonardo and Benacchio, Tommaso and Bonaventura, Luca and Bungartz, Hans-Joachim and Chatterjee, Sanjay and Ciorba, Florina M and DeBardeleben, Nathan and Drzisga, Daniel and Eibl, Sebastian and Engelmann, Christian and Gansterer, Wilfried N and Giraud, Luc and G{\"o}ddeke, Dominik and Heisig, Marco and J{\'e}z{\'e}quel, Fabienne and Kohl, Nils and Li, Xiaoye Sherry and Lion, Romain and Mehl, Miriam and Mycek, Paul and Obersteiner, Michael and Quintana-Ort{\'\i}, Enrique S and Rizzi, Francesco and R{\"u}de, Ulrich and Schulz, Martin and Fung, Fred and Speck, Robert and Stals, Linda and Teranishi, Keita and Thibault, Samuel and Th{\"o}nnes, Dominik and Wagner, Andreas and Wohlmuth, Barbara}
}
