In this paper, we present the VOLNA-OP2 tsunami model and implementation; a finite-volume non-linear shallow-water equation (NSWE) solver built on the OP2 domain-specific language (DSL) for unstructured mesh computations. VOLNA-OP2 is unique among tsunami solvers in its support for several high-performance computing platforms: Central processing units (CPUs), the Intel Xeon Phi, and graphics processing units (GPUs). This is achieved in a way that the scientific code is kept separate from various parallel implementations, enabling easy maintainability. It has already been used in production for several years; here we discuss how it can be integrated into various workflows, such as a statistical emulator. The scalability of the code is demonstrated on three supercomputers, built with classical Xeon CPUs, the Intel Xeon Phi, and NVIDIA P100 GPUs. VOLNA-OP2 shows an ability to deliver productivity as well as performance and portability to its users across a number of platforms.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We would like to thank Endre László, formerly of PPCU ITK, who worked on the initial port of VOLNA to OP2. István Z. Reguly was supported by the János Bólyai Research Scholarship of the Hungarian Academy of Sciences. Project no. PD 124905 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the PD_17 funding scheme. The authors would like to acknowledge the use of the University of Oxford Advanced Research Computing (ARC) facility in carrying out this work (https://doi.org/10.5281/zenodo.22558). Serge Guillas gratefully acknowledges support through the NERC grants PURE (Probability, Uncertainty and Risk in the Natural Environment) NE/J017434/1 and “A demonstration tsunami catastrophe risk model for the insurance industry” NE/L002752/1. Serge Guillas and Devaraj Gopinathan acknowledge support from the NERC project (NE/P016367/1) under the Global Challenges Research Fund: Building Resilience programme. Devaraj Gopinathan acknowledges support from the Royal Society, UK, and Science and Engineering Research Board (SERB), India, for the Royal Society–SERB Newton International Fellowship (NF151483). Daniel Giles acknowledges support by the Irish Research Council’s Postgraduate Scholarship Programme.