Veröffentlichungen "Seiferth, Johannes"

2022

Hacker, Oliver ; Korch, Matthias ; Seiferth, Johannes:
A Motivating Case Study on Code Variant Selection by Reinforcement Learning.
In: Varbanescu, Ana-Lucia ; Bhatele, Abhinav ; Luszczek, Piotr ; Marc, Baboulin (Hrsg.): High Performance Computing : Proceedings. - Cham : Springer , 2022 . - S. 293-312 . - (Lecture Notes in Computer Science ; 13289 )
ISBN 978-3-031-07312-0
DOI: https://doi.org/10.1007/978-3-031-07312-0_15

2021

Alappat, Christie L. ; Seiferth, Johannes ; Hager, Georg ; Korch, Matthias ; Rauber, Thomas ; Wellein, Gerhard:
YaskSite : Stencil Optimization Techniques Applied to Explicit ODE Methods on Modern Architectures.
In: Lee, Jae W. ; Soffa, Mary Lou ; Zaks, Ayal (Hrsg.): 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO) : Proceedings. - Piscataway, NJ , 2021 . - S. 174-186
ISBN 978-1-7281-8613-9
DOI: https://doi.org/10.1109/CGO51591.2021.9370316

2020

Seiferth, Johannes ; Korch, Matthias ; Rauber, Thomas:
Offsite Autotuning Approach : Performance Model Driven Autotuning Applied to Parallel Explicit ODE Methods.
In: Sadayappan, Ponnuswamy ; Chamberlain, Bradford L. ; Juckeland, Guido ; Ltaief, Hatem (Hrsg.): High Performance Computing. - Cham : Springer , 2020 . - S. 370-390 . - (Lecture Notes in Computer Science ; 12151 )
ISBN 978-3-030-50743-5
DOI: https://doi.org/10.1007/978-3-030-50743-5_19

2019

Scherg, Markus ; Seiferth, Johannes ; Korch, Matthias ; Rauber, Thomas:
Performance Prediction of Explicit ODE Methods on Multi-Core Cluster Systems.
In: Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering. - New York, NY : ACM , 2019 . - S. 139-150
ISBN 978-1-4503-6239-9
DOI: https://doi.org/10.1145/3297663.3310306

2018

Seiferth, Johannes ; Alappat, Christie L. ; Korch, Matthias ; Rauber, Thomas:
Applicability of the ECM Performance Model to Explicit ODE Methods on Current Multi-core Processors.
In: Yokota, Rio ; Weiland, Michèle ; Keyes, David ; Trinitis, Carsten (Hrsg.): High Performance Computing. - Cham : Springer , 2018 . - S. 163-183
ISBN 978-3-319-92040-5
DOI: https://doi.org/10.1007/978-3-319-92040-5_9