This repository contains the code for (corrected) numerical experiments in the paper 'A fast algorithm with error bounds for Quadrature by Expansion,' available at doi:10.1016/j.jcp.2018.05.006 or on arXiv.

The code that is included reproduces the following parts of the paper:

• Tables 3, 4, 5, and 6
• Figures 14 and 15
• Data presented in Section 5.2.2

Install the Docker image, and from a shell running in a container, go to the code directory and type:

./run.sh

This script re-runs all experiments, and generates an output file summary/summary.pdf containing all the generated figures, tables, and data.

It's also possible to have more selective control over what gets run. See below.

This code uses a special branch of Pytential, available here. Two options are available for obtaining the needed revisions of Pytential and its dependencies.

The simplest way to install is to use the Docker image. The code and software are installed in the image directory /home/inteq/gigaqbx-2d-results.

If you don't want to use the Docker image, you can install necessary software manually using the command:

./install.sh

This script downloads and installs software in an isolated environment in this directory using Conda and pip.

For producing figures and outputs, you also need LaTeX and the latexrun script.

Before directly running the Python scripts in this directory, activate the Conda environment and instruct PyOpenCL to use POCL, as follows:

source .miniconda3/bin/activate inteq
export PYOPENCL_TEST=portable

To ensure that everything works, you can run a short test:

py.test --disable-warnings utils.py

A more extensive set of tests can be found in the Pytential test suite (included in src/pytential/test), which should also pass.

The scripts generate-data.py and generate-figures-and-tables.py can be used to run individual experiments or groups of experiments, and postprocess experimental outputs, respectively. Pass the --help option for more documentation and the list of available experiments.

The raw-data directory is written to by generate-data.py and holds experimental outputs. The out directory contains generated figures and tables and is written to by generate-figures-and-tables.py.

To regenerate all outputs from the data that is already in the raw-data directory, run

./generate-figures-and-tables.py --all
make -f makefile.summary

To run an individual experiment or to regenerate the data for a single experiments, supply the command line option -x experiment-name. For instance, to regenerate the results for the bvp experiment, run

./generate-data.py -x bvp
./generate-figures-and-tables.py -x bvp

The following files and directories in this repository are included and/or generated:

To cite the paper:

@article{gigaqbx2d,
  title = "A fast algorithm with error bounds for {Quadrature} by {Expansion}",
  journal = "Journal of Computational Physics",
  volume = "374",
  pages = "135 - 162",
  year = "2018",
  doi = "10.1016/j.jcp.2018.05.006",
  author = "Matt Wala and Andreas Klöckner",
}

To cite the repository and/or Docker image:

@software{gigaqbx2d_repo,
  author       = {Matt Wala and
                  Andreas Klöckner},
  title        = {mattwala/gigaqbx-2d-paper-code: Initial release},
  month        = nov,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.3530946},
  url          = {https://doi.org/10.5281/zenodo.3530946}
}

@software{gigaqbx2d_docker,
  author       = {Wala, Matt},
  title        = {{Docker image: A fast algorithm with error bounds 
                   for Quadrature by Expansion}},
  month        = nov,
  year         = 2019,
  publisher    = {Zenodo},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.3483367},
  url          = {https://doi.org/10.5281/zenodo.3483367}
}