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Johannes Seiferth

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Conference and Workshop Papers

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2022
[c5]
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  - conf/supercomputer/HackerKS22
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  - https://dblp.org/rec/conf/supercomputer/HackerKS22
Oliver Hacker, Matthias Korch, Johannes Seiferth:
A Motivating Case Study on Code Variant Selection by Reinforcement Learning. ISC 2022: 293-312
2021
[c4]
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  - conf/cgo/AlappatSHKRW21
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Christie L. Alappat, Johannes Seiferth, Georg Hager, Matthias Korch, Thomas Rauber, Gerhard Wellein:
YaskSite: Stencil Optimization Techniques Applied to Explicit ODE Methods on Modern Architectures. CGO 2021: 174-186
2020
[c3]
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Johannes Seiferth, Matthias Korch, Thomas Rauber:
Offsite Autotuning Approach - Performance Model Driven Autotuning Applied to Parallel Explicit ODE Methods. ISC 2020: 370-390
2019
[c2]
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  - conf/wosp/SchergSKR19
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Markus Scherg, Johannes Seiferth, Matthias Korch, Thomas Rauber:
Performance Prediction of Explicit ODE Methods on Multi-Core Cluster Systems. ICPE 2019: 139-150
2018
[c1]
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Johannes Seiferth, Christie L. Alappat, Matthias Korch, Thomas Rauber:
Applicability of the ECM Performance Model to Explicit ODE Methods on Current Multi-core Processors. ISC 2018: 163-183

Informal and Other Publications

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2020
[i1]
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  - journals/corr/abs-2004-03695
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Johannes Seiferth, Matthias Korch, Thomas Rauber:
Offsite Autotuning Approach - Performance Model Driven Autotuning Applied to Parallel Explicit ODE Methods. CoRR abs/2004.03695 (2020)

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