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High-order numerical methods for unstructured grids combine the superioraccuracy of high-order spectral or finite difference methods with the geometricflexibility of low-order finite volume or finite element schemes. The FluxReconstruction (FR) approach unifies various high-order schemes forunstructured grids within a single framework. Additionally, the FR approachexhibits a significant degree of element locality, and is thus able to runefficiently on modern streaming architectures, such as Graphical ProcessingUnits (GPUs). The aforementioned properties of FR mean it offers a promisingroute to performing affordable, and hence industrially relevant,scale-resolving simulations of hitherto intractable unsteady flows within thevicinity of real-world engineering geometries. In this paper we present PyFR,an open-source Python based framework for solving advection-diffusion typeproblems on streaming architectures using the FR approach. The framework isdesigned to solve a range of governing systems on mixed unstructured gridscontaining various element types. It is also designed to target a range ofhardware platforms via use of an in-built domain specific language based on theMako templating engine. The current release of PyFR is able to solve thecompressible Euler and Navier-Stokes equations on grids of quadrilateral andtriangular elements in two dimensions, and hexahedral elements in threedimensions, targeting clusters of CPUs, and NVIDIA GPUs. Results are presentedfor various benchmark flow problems, single-node performance is discussed, andscalability of the code is demonstrated on up to 104 NVIDIA M2090 GPUs. Thesoftware is freely available under a 3-Clause New Style BSD license (seewww.pyfr.org).

PyFR: An Open Source Framework for Solving Advection-Diffusion Type Problems on Streaming Architectures using the Flux Reconstruction Approach