Gravo MG: Graph Voronoi Multigrid

[Paper] [Project page] [C++ library] [Python Bindings]

Repository for "A Fast Geometric Multigrid Method for Curved Surfaces", published at SIGGRAPH 2023
by Ruben Wiersma, Ahmad Nasikun (equal contribution); Elmar Eisemann; Klaus Hildebrandt.

If you need to solve linear systems on meshes or point clouds with >50.000 vertices, Gravo MG is what you need. Gravo MG is a fast geometric multigrid method that quickly computes a hierarchy used in an iterative multigrid solver. The use of graph Voronoi cells enables fast convergence, while allowing for fast construction.

Standalone C++ and Python libraries

This repository contains many components that are not strictly relevant for using Gravo MG (comparisons, related work, etc.). It is recommended to use the following packages, instead of this repository:

• Gravo MG C++ library
• Gravo MG Python bindings

For Python, that means you can use

pip install gravomg

and start solving. More info can be found in the Python binding repository.

Replicating our results

The tables in our paper are created using the scripts in experiments/table_scripts. These scripts require you to build and install a Python package using pip.

Setting up the environment

1. Clone this repository, including the submodules (required to pull in Pybind11), and change into the directory:

git clone --recursive https://github.com/rubenwiersma/gravo_mg.git
cd gravo_mg

If you forgot to use the --recursive flag, you can pull the required submodules with

git submodule update --init --recursive

2. Create a Conda environment with the necessary requirements and activate environment:

conda env create -f environment.yml
conda activate gravomg

3. Install the gravomg Python package:

pip install -e ./gravomg_bindings

This builds the Gravo MG C++ library and wraps it in a Python binding. The -e flag installs the package in the current folder, rather than your default Conda folder.

Running the experiments

First, download the data, unzip, and place it in the gravo_mg folder. The result should be a folder data in the root of this repository.

You can run each experiment from the experiments folder, e.g.:

sh experiments/table_scripts/comparison_poisson.sh

The output is written in out/timing and a formatted LaTeX table in out/latex.

Pardiso

To compute the timings for Pardiso, make sure that you have installed Intel's OneMKL on your machine. Our library automatically looks for OneMKL in /usr/include/mkl and enables it for use when compiling. You can change the search path in gravomg/CMakeLists.txt.

If you've already installed the gravomg package before installing OneMKL, make sure that you reinstall gravomg.

Acknowledgements

Code

Our code uses Derek Liu and colleagues' code for Surface Multigrid via Intrinsic Prolongation to replicate their results and as a basis for the main solver routine.

Data

You can download the data used in the paper here: download the data.

Please find the source and author of each mesh used in our experiments in the linked sheet.

Citations

Please cite our paper if this code contributes to an academic publication:

@Article{WiersmaNasikun2023GravoMG,
author = {Ruben Wiersma, Ahmad Nasikun, Elmar Eisemann, Klaus Hildebrandt},
journal = {SIGGRAPH 2023},
title = {A Fast Geometric Multigrid Method for Curved Surfaces},
year = {2023},
month = jul,
number = {4},
volume = {41},
doi = {10.1145/3588432.3591502},
publisher = {ACM}
}