Preparing an incompressible-flow fluid dynamics code for exascale-class wind energy simulations

• Main Authors: Mullowney, Paul, Li, Ruipeng, Thomas, Stephen, Ananthan, Shreyas, Sharma, Ashesh, Rood, Jon S., Williams, Alan B., Sprague, Michael A.
• Format: Conference Proceeding
• Language: English
• Subjects: Algebraic Multigrid; Applied computing; Applied computing > Physical sciences and engineering; CFD; Computational fluid dynamics; Computational Linear Algebra; Computational modeling; Computing methodologies; Computing methodologies > Modeling and simulation; GPU Computing; Graphics processing units; Overset; Performance evaluation; Predictive models; Wind energy; Wind farms
• ISSN: 2167-4337
• DOI: 10.1145/3458817.3476185

The U.S. Department of Energy has identified exascale-class wind farm simulation as critical to wind energy scientific discovery. A primary objective of the ExaWind project is to build high-performance, predictive computational fluid dynamics (CFD) tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next-generation, exascale-class supercomputers. Here, we report on our efforts in preparing the ExaWind unstructured mesh solver, Nalu-Wind, for exascale-class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is insufficient. To achieve high performance, one needs novel algorithms that are application aware, memory efficient, and optimized for the latest-generation GPU devices. The result of our efforts are unstructured-mesh simulations of wind turbines that can effectively leverage thousands of GPUs. In particular, we demonstrate a first-of-its-kind, incompressible-flow simulation using Algebraic Multigrid solvers that strong scales to more than 4000 GPUs on the Summit supercomputer.