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A Fast and Flexible CFD Solver with Heterogeneous Execution - JuliaCon 2024

Offered By: The Julia Programming Language via YouTube

Tags

Computational Fluid Dynamics Courses Machine Learning Courses Julia Courses Parallel Computing Courses GPU Computing Courses Heterogeneous Computing Courses Differentiable Programming Courses

Course Description

Overview

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Explore a conference talk from JuliaCon 2024 that delves into the evolution of WaterLily.jl, a computational fluid dynamics solver in Julia. Learn how this CFD solver transitioned from a serial-CPU implementation to a backend-agnostic solution capable of seamless execution across multi-threaded CPUs and various GPU vendors. Discover the meta-programming approach used to generalize array iterator implementation and the utilization of KernelAbstractions.jl for architecture-specific kernel specialization. Examine performance comparisons showing WaterLily.jl matching state-of-the-art CFD solvers written in C++ or Fortran in single-GPU tests. Gain insights into the potential integration of machine learning models and differentiability into the solver, expanding its capabilities for future applications.

Syllabus

A fast and flexible CFD solver with heterogeneous execution | Weymouth, Font | JuliaCon 2024


Taught by

The Julia Programming Language

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