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OpenSBLI: Automated code-generation for heterogeneous computing architectures applied to compressible fluid dynamics on structured grids

David J. Lusher, Satya P. Jammy, Neil D. Sandham
Aerodynamics and Flight Mechanics group, University of Southampton. Boldrewood Campus, Southampton, SO16 7QF, United Kingdom
arXiv:2007.14933 [physics.comp-ph], (29 Jul 2020)

@misc{lusher2020opensbli,

   title={OpenSBLI: Automated code-generation for heterogeneous computing architectures applied to compressible fluid dynamics on structured grids},

   author={David J. Lusher and Satya P. Jammy and Neil D. Sandham},

   year={2020},

   eprint={2007.14933},

   archivePrefix={arXiv},

   primaryClass={physics.comp-ph}

}

OpenSBLI is an open-source code-generation system for compressible fluid dynamics (CFD) on heterogeneous computing architectures. Written in Python, OpenSBLI is an explicit high-order finite-difference solver on structured curvilinear meshes. Shock-capturing is performed by a choice of high-order Weighted Essentially Non-Oscillatory (WENO) or Targeted Essentially Non-Oscillatory (TENO) schemes. OpenSBLI generates a complete CFD solver in the Oxford Parallel Structured (OPS) domain specific language. The OPS library is embedded in C code, enabling massively-parallel execution of the code on a variety of high-performance-computing architectures, including GPUs. The present paper presents a code base that has been completely rewritten from the earlier proof of concept (Jacobs et al, JoCS 18 (2017), 12-23), allowing shock capturing, coordinate transformations for complex geometries, and a wide range of boundary conditions, including solid walls with and without heat transfer. A suite of validation and verification cases are presented, plus demonstration of a large-scale Direct Numerical Simulation (DNS) of a transitional Shockwave Boundary Layer Interaction (SBLI). The code is shown to have good weak and strong scaling on multi-GPU clusters. We demonstrate that code-generation and domain specific languages are suitable for performing efficient large-scale simulations of complex fluid flows on emerging computing architectures.
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