ExaHyPe
ExaHyPE is a high-performance engine for solving hyperbolic partial differential equations (PDEs). It uses high-order discontinuous Galerkin methods with ADER time-stepping on dynamically adaptive, tree-structured Cartesian meshes, allowing efficient and accurate simulations of complex physical systems.
Within ChEESE-2P, ExaHyPE focuses on seismic applications through ExaSeis, which models seismic wave propagation and dynamic rupture on curvilinear meshes. It also supports shallow-water models and other hyperbolic PDE systems, making it a versatile tool for geophysical research.
ExaHyPE is integrated with the MIT Uncertainty Quantification (MUQ) library, enabling robust analysis of uncertainties in simulations. Its combination of advanced numerical methods, adaptive meshes, and uncertainty quantification makes it a powerful platform for large-scale geophysical modeling and research.
Significant publications related to the code development
- Marc Marot-Lassauzaie, Michael Bader: Mixed-Precision in High-Order Methods: the Impact of Floating-Point Precision on the ADER-DG Algorithm. Submitted to ACM Transactions on Mathematical Software. Preprint on https://arxiv.org/abs/2504.06889.
- Mario Wille; Tobias Weinzierl; Gonzalo Brito Gadeschi; Michael Bader: Efficient GPU Offloading with OpenMP for a Hyperbolic Finite Volume Solver on Dynamically Adaptive Meshes – HPC Algorithms and Applications. In: High Performance Computing. Springer (13948. Aufl.), 2023, 65-85. https://dx.doi.org/https://doi.org/10.1007/978-3-031-32041-5_4
- Li, B., Schulz, H., Weinzierl, T., & Zhang, H. (2022). Dynamic task fusion for a block-structured finite volume solver over a dynamically adaptive mesh with local time stepping. In: High Performance Computing 37th International Conference, ISC High Performance 2022, Hamburg, Germany, May 29 – June 2, 2022, Proceedings (pp. 153-173). Springer Verlag. https://doi.org/10.1007/978-3-031-07312-0_8
- Linus Seelinger; Anne Reinarz; Leonhard Rannabauer; Michael Bader; Peter Bastian; Robert Scheichl: High Performance Uncertainty Quantification with Parallelized Multilevel Markov Chain Monte Carlo. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC21) (SC ’21), Association for Computing Machinery, 2021. https://dx.doi.org/10.1145/3458817.3476150
- Reinarz, Anne; Charrier, Dominic; Bader, Michael; Bovard, Luke; Dumbser, Michael; Duru, Kenneth; Fambri, Francesco; Gabriel, Alice-Agnes; Gallard, Jean-Matthieu; Koeppel, Sven Köppel; Krenz, Lukas; Rannabauer, Leonhard; Rezzolla, Luciano; Samfass, Philipp; Tavelli, Maurizio; Weinzierl, Tobias: ExaHyPE: An Engine for Parallel Dynamically Adaptive Simulations of Wave Problems. Computer Physics Communications 254, 2020, 107251. https://dx.doi.org/10.1016/j.cpc.2020.107251
To Know More
License
Copyright (c) 2016, The ExaHyPE Consortium, http://exahype.eu All rights reserved.
Open Source License
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
