Streamlining accelerated computing for industry
PyFR code combines high accuracy with flexibility to resolve unsteady turbulence problems
DOE/Oak Ridge National Laboratory
IMAGE: A simulation of flow over five jet engine low-pressure turbine blades. In an effort to update computational fluid dynamics (CFD) for modern hardware platforms, a group of Imperial College researchers… view more
Credit: Imperial College
Scientists and engineers striving to create the next machine-age marvel–whether it be a more aerodynamic rocket, a faster race car, or a higher-efficiency jet engine–depend on reliable analysis and feedback to improve their designs.
Building and testing physical prototypes of complex machines can be time-consuming and costly and can provide only limited results. For these reasons, companies in industries as diverse as aerospace, car manufacturing, and wind power have been turning to supercomputers to investigate complex design problems related to fluid flow, or how air and fluids interact with a machine.
Using computational fluid dynamics (CFD) applications, codes created to track fluid’s chaotic flow patterns through or around a solid object, researchers can augment physical testing, gain a more comprehensive view of product performance, and even glean insights that might lead to further design improvements.
But as supercomputers have increased in size and scale, many industry-standard CFD applications have struggled to keep pace, limiting their accuracy and ability to fully supplant physical testing. Furthermore, many CFD codes have not yet been adapted for accelerated computing architectures, such as that of the Oak Ridge Leadership Computing Facility’s (OLCF’s) Titan supercomputer, a Cray XK7 with a peak performance of 27 petaflops.
In an effort to modernize CFD, a group of Imperial College researchers led by Peter Vincent, a senior lecturer in the Department of Aeronautics, has developed new open-source software called PyFR, a Python-based application that combines highly accurate numerical methods with a highly flexible, portable, and scalable code implementation that makes efficient use of accelerators like Titan’s GPUs. Industry adoption of the code could allow companies to better exploit petascale computing to understand long-standing fluid flow problems, in particular unsteady turbulence–the seemingly random and chaotic motion of air, water, and other fluids.
In recognition for its work, Vincent’s team, which includes postdoctoral researchers Brian Vermeire, Jin Seok Park, and Arvind Iyer of Imperial College and postdoctoral researcher Freddie Witherden of Stanford University, has been named a 2016 finalist for the Association of Computer Machinery’s Gordon Bell Prize, one of the most prestigious prizes in supercomputing.
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