Lettuce is a Computational Fluid Dynamics framework based on the lattice Boltzmann method (LBM).
It provides
- GPU-accelerated computation based on PyTorch
- Rapid Prototyping in 2D and 3D
- Usage of neural networks and automatic differentiation within LBM
- Custom PyTorch extensions for optimized native CUDA kernels
When using lettuce please cite:
@inproceedings{bedrunka2021lettuce,
title={Lettuce: PyTorch-Based Lattice Boltzmann Framework},
author={Bedrunka, Mario Christopher and Wilde, Dominik and Kliemank, Martin and Reith, Dirk and Foysi, Holger and Kr{\"a}mer, Andreas},
booktitle={High Performance Computing: ISC High Performance Digital 2021 International Workshops, Frankfurt am Main, Germany, June 24--July 2, 2021, Revised Selected Papers},
pages={40},
organization={Springer Nature}
}
The following Python code will run a two-dimensional Taylor-Green vortex on a GPU:
import torch
from lettuce import BGKCollision, StandardStreaming, Lattice, D2Q9, TaylorGreenVortex2D, Simulation
device = "cuda:0" # for running on cpu: device = "cpu"
dtype = torch.float32
lattice = Lattice(D2Q9, device, dtype)
flow = TaylorGreenVortex2D(resolution=256, reynolds_number=10, mach_number=0.05, lattice=lattice)
collision = BGKCollision(lattice, tau=flow.units.relaxation_parameter_lu)
streaming = StandardStreaming(lattice)
simulation = Simulation(flow=flow, lattice=lattice, collision=collision, streaming=streaming)
mlups = simulation.step(num_steps=1000)
print("Performance in MLUPS:", mlups)More advanced examples are available as jupyter notebooks:
Install the anaconda package manager from www.anaconda.org
Create a new conda environment and install all dependencies:
conda create -n lettuce -c pytorch -c conda-forge\ "pytorch>=1.2" matplotlib pytest click cudatoolkit "pyevtk>=1.2" mmh3Activate the conda environment:
conda activate lettuce
Clone this repository from github
Change into the cloned directory
Run the install script:
python setup.py install
Run the test cases:
python setup.py test
Check out the convergence order, running on CPU:
lettuce --no-cuda convergence
For running a CUDA-driven LBM simulation on one GPU omit the --no-cuda. If CUDA is not found, make sure that cuda drivers are installed and compatible with the installed cudatoolkit (see conda install command above).
Check out the performance, running on GPU:
lettuce benchmark
We use the following third-party packages:
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
- Free software: MIT license, as found in the LICENSE file.