Team > Johannes Seiferth, M.Sc.

2022

Hacker, Oliver; Korch, Matthias; Seiferth, Johannes
A Motivating Case Study on Code Variant Selection by Reinforcement Learning
High Performance Computing : Proceedings
Cham : Springer, 2022. - S. 293-312 . - (Lecture Notes in Computer Science; 13289)
doi: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 Architectu ...
2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO) : Proceedings
Piscataway, NJ , 2021. - S. 174-186
doi:10.1109/CGO51591.2021.9370316 ...

2020

Seiferth, Johannes; Korch, Matthias; Rauber, Thomas
Offsite Autotuning Approach : Performance Model Driven Autotuning Applied to Parallel Explicit ...
High Performance Computing
Cham : Springer, 2020. - S. 370-390 . - (Lecture Notes in Computer Science; 12151)
doi: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
Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering
New York, NY : ACM, 2019. - S. 139-150
doi: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 Proces ...
High Performance Computing
Cham : Springer, 2018. - S. 163-183
doi:10.1007/978-3-319-92040-5_9 ...