Jeffrey Cornelis
Title
Cited by
Cited by
Year
The communication-hiding conjugate gradient method with deep pipelines
J Cornelis, S Cools, W Vanroose
arXiv preprint arXiv:1801.04728, 2018
182018
Numerically stable recurrence relations for the communication hiding pipelined conjugate gradient method
S Cools, J Cornelis, W Vanroose
IEEE Transactions on Parallel and Distributed Systems 30 (11), 2507-2522, 2019
72019
Improving strong scaling of the conjugate gradient method for solving large linear systems using global reduction pipelining
S Cools, J Cornelis, P Ghysels, W Vanroose
arXiv preprint arXiv:1905.06850, 2019
32019
Projected Newton method for noise constrained Tikhonov regularization
J Cornelis, N Schenkels, W Vanroose
Inverse Problems 36 (5), 055002, 2020
22020
Projected Newton method for noise constrained ℓ p regularization
J Cornelis, W Vanroose
Inverse Problems 36 (12), 125004, 2020
2020
Projected Newton method for noise constrained regularization
J Cornelis, W Vanroose
arXiv preprint arXiv:2005.02687, 2020
2020
Numerical Analysis of the Maximal Attainable Accuracy in Communication-hiding Pipelined Conjugate Gradients
S Cools, J Cornelis, E Agullo, E Fatih-Yetkin, L Giraud, W Vanroose
CSE19-SIAM Conference on Computational Science and Engineering, 2019
2019
Constructie, numerieke eigenschappen en parallelle performantie van communicatie reducerende pipelined Krylov deelruimte methoden
J Cornelis
Universiteit Antwerpen, 2017
2017
Hiding Global Reduction Latency in Pipelined Krylov Methods
S Cools, J Cornelis, W Vanroose, P Ghysels, EF Yetkin, E Agullo, ...
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