Preparing an Incompressible-Flow Fluid Dynamics Code for Exascale-Class Wind Energy Simulations

Paul Mullowney, Ruipeng Li, Stephen Thomas, Shreyas Ananthan, Ashesh Sharma, Jon Rood, Alan Williams, Michael Sprague

Research output: NRELPresentation


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.
Original languageAmerican English
Number of pages19
StatePublished - 2021

Publication series

NamePresented at the International Conference for High Performance Computing, Networking, Storage, and Analysis, 14-19 November 2021, St. Louis, Missouri

NREL Publication Number

  • NREL/PR-2C00-81212


  • algebraic multigrid
  • CFD
  • computational linear algebra
  • exascale
  • GPU computing


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