Articles | Volume 11, issue 11
https://doi.org/10.5194/gmd-11-4621-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-4621-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
19 Nov 2018
Development and technical paper |
| 19 Nov 2018
| 19 Nov 2018
The VOLNA-OP2 tsunami code (version 1.5)
Istvan Z. Reguly
CORRESPONDING AUTHOR
Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Prater u 50/a, 1088 Budapest, Hungary
Daniel Giles
School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
Devaraj Gopinathan
Department of Statistical Science, University College London, London, UK
Laure Quivy
Centre de Mathématiques et de Leurs Applications (CMLA), Ecole Normale Supérieure, Paris-Saclay, Centre National de la Recherche Scientifique, Université Paris-Saclay, 94235 Cachan, France
Joakim H. Beck
Computer, Electrical and Mathematical Science and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
Michael B. Giles
Math Institute, University of Oxford, Oxford, UK
Serge Guillas
Department of Statistical Science, University College London, London, UK
Frederic Dias
School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
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Cited
15 citations as recorded by crossref.
- Performance and efficiency: A multi-generational benchmark of modern processors on bandwidth-bound HPC applications B. Drávai & I. Reguly https://doi.org/10.1016/j.future.2025.107793
- Modelling with Volna-OP2—Towards Tsunami Threat Reduction for the Irish Coastline D. Giles et al. https://doi.org/10.3390/geosciences10060226
- basement v3: A modular freeware for river process modelling over multiple computational backends D. Vanzo et al. https://doi.org/10.1016/j.envsoft.2021.105102
- Probabilistic quantification of tsunami current hazard using statistical emulation D. Gopinathan et al. https://doi.org/10.1098/rspa.2021.0180
- Faster Than Real Time Tsunami Warning with Associated Hazard Uncertainties D. Giles et al. https://doi.org/10.3389/feart.2020.597865
- Meteotsunamis and other anomalous “tidal surge” events in Western Europe in Summer 2022 E. Renzi et al. https://doi.org/10.1063/5.0139220
- Productivity, performance, and portability for computational fluid dynamics applications I. Reguly & G. Mudalige https://doi.org/10.1016/j.compfluid.2020.104425
- The VOLNA-OP2 tsunami code (version 1.5) I. Reguly et al. https://doi.org/10.5194/gmd-11-4621-2018
- Modeling and Simulation of Tsunami Impact: A Short Review of Recent Advances and Future Challenges S. Marras & K. Mandli https://doi.org/10.3390/geosciences11010005
- Locality optimized unstructured mesh algorithms on GPUs A. Sulyok et al. https://doi.org/10.1016/j.jpdc.2019.07.011
- Uncertainty in Manning’s roughness coefficient in multilevel simulations of future tsunamis in Sumatra K. Li et al. https://doi.org/10.1098/rspa.2024.0637
- Performance analysis of Volna-OP2 – massively parallel code for tsunami modelling D. Giles et al. https://doi.org/10.1016/j.compfluid.2020.104649
- Probabilistic, high-resolution tsunami predictions in northern Cascadia by exploiting sequential design for efficient emulation D. Salmanidou et al. https://doi.org/10.5194/nhess-21-3789-2021
- A unified object-oriented framework for CPU+GPU explicit hyperbolic solvers D. Conde et al. https://doi.org/10.1016/j.advengsoft.2020.102802
- A real-time prediction model for instantaneous dam-break flood evolution of concrete gravity dams based on attention mechanism and spatiotemporal multiple features C. Wang et al. https://doi.org/10.1016/j.engappai.2025.110616
15 citations as recorded by crossref.
- Performance and efficiency: A multi-generational benchmark of modern processors on bandwidth-bound HPC applications B. Drávai & I. Reguly https://doi.org/10.1016/j.future.2025.107793
- Modelling with Volna-OP2—Towards Tsunami Threat Reduction for the Irish Coastline D. Giles et al. https://doi.org/10.3390/geosciences10060226
- basement v3: A modular freeware for river process modelling over multiple computational backends D. Vanzo et al. https://doi.org/10.1016/j.envsoft.2021.105102
- Probabilistic quantification of tsunami current hazard using statistical emulation D. Gopinathan et al. https://doi.org/10.1098/rspa.2021.0180
- Faster Than Real Time Tsunami Warning with Associated Hazard Uncertainties D. Giles et al. https://doi.org/10.3389/feart.2020.597865
- Meteotsunamis and other anomalous “tidal surge” events in Western Europe in Summer 2022 E. Renzi et al. https://doi.org/10.1063/5.0139220
- Productivity, performance, and portability for computational fluid dynamics applications I. Reguly & G. Mudalige https://doi.org/10.1016/j.compfluid.2020.104425
- The VOLNA-OP2 tsunami code (version 1.5) I. Reguly et al. https://doi.org/10.5194/gmd-11-4621-2018
- Modeling and Simulation of Tsunami Impact: A Short Review of Recent Advances and Future Challenges S. Marras & K. Mandli https://doi.org/10.3390/geosciences11010005
- Locality optimized unstructured mesh algorithms on GPUs A. Sulyok et al. https://doi.org/10.1016/j.jpdc.2019.07.011
- Uncertainty in Manning’s roughness coefficient in multilevel simulations of future tsunamis in Sumatra K. Li et al. https://doi.org/10.1098/rspa.2024.0637
- Performance analysis of Volna-OP2 – massively parallel code for tsunami modelling D. Giles et al. https://doi.org/10.1016/j.compfluid.2020.104649
- Probabilistic, high-resolution tsunami predictions in northern Cascadia by exploiting sequential design for efficient emulation D. Salmanidou et al. https://doi.org/10.5194/nhess-21-3789-2021
- A unified object-oriented framework for CPU+GPU explicit hyperbolic solvers D. Conde et al. https://doi.org/10.1016/j.advengsoft.2020.102802
- A real-time prediction model for instantaneous dam-break flood evolution of concrete gravity dams based on attention mechanism and spatiotemporal multiple features C. Wang et al. https://doi.org/10.1016/j.engappai.2025.110616
Saved (final revised paper)
Latest update: 11 Jun 2026
Short summary
We present the VOLNA-OP2 tsunami simulation code, built on the OP2 library. It is unique among such solvers in its support for several high-performance computing platforms: CPUs, the Intel Xeon Phi, and GPUs. This is achieved in a way that the scientific code is kept separate from various parallel implementations, enabling easy maintainability. Scalability and efficiency are demonstrated on three supercomputers built with CPUs, Xeon Phi's, and GPUs.
We present the VOLNA-OP2 tsunami simulation code, built on the OP2 library. It is unique among...
