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Efficient Computation of Large-Scale Statistical Solutions to Incompressible Fluid Flows

Published: 03 June 2024 Publication History

Abstract

This work presents the development, performance analysis and subsequent optimization of a GPU-based spectral hyperviscosity solver for turbulent flows described by the three dimensional incompressible Navier-Stokes equations. The method solves for the fluid velocity fields directly in Fourier space, eliminating the need to solve a large-scale linear system of equations in order to find the pressure field. Special focus is put on the communication intensive transpose operation required by the fast Fourier transform when using distributed memory parallelism. After multiple iterations of benchmarking and improving the code, the simulation achieves close to optimal performance on the Piz Daint supercomputer cluster, even outperforming the Cray MPI implementation on Piz Daint in its communication routines. This optimal performance enables the computation of large-scale statistical solutions of incompressible fluid flows in three space dimensions.

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cover image ACM Conferences
PASC '24: Proceedings of the Platform for Advanced Scientific Computing Conference
June 2024
296 pages
ISBN:9798400706394
DOI:10.1145/3659914
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 03 June 2024

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Author Tags

  1. computational fluid dynamics
  2. direct numerical simulation
  3. GPU accelerated simulation

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PASC '24
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PASC '24 Paper Acceptance Rate 26 of 36 submissions, 72%;
Overall Acceptance Rate 109 of 221 submissions, 49%

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