GBGPU is a GPU-accelerated version of the FastGB waveform which has been developed by Neil Cornish, Tyson Littenberg, Travis Robson, and Stas Babak. It computes gravitational waveforms for Galactic binary systems observable by LISA using a fast/slow-type decomposition. For more details on the original construction of FastGB see arXiv:0704.1808.

The current version of the code is very closely related to the implementation of FastGB in the LISA Data Challenges' Python code package. The waveform code is entirely Python-based. It is about 1/2 the speed of the full C version, but much simpler in Python for right now. There are also many additional functions including fast likelihood computations for individual Galactic binaries, as well as fast C-based methods to combine waveforms into global fitting templates.

The code is CPU/GPU agnostic. CUDA and NVIDIA GPUs are required to run these codes for GPUs.

See the documentation for more details. This code was designed for arXiv:2205.03461. If you use any part of this code, please cite arXiv:2205.03461, its Zenodo page, arXiv:0704.1808, and arXiv:1806.00500.

Below is a quick set of instructions to get you started with gbgpu.

0. Install Anaconda if you do not have it.

1. Create a virtual environment. Note: There is no available conda compiler for Windows. If you want to install for Windows, you will probably need to add libraries and include paths to the setup.py file.

conda create -n gbgpu_env -c conda-forge gcc_linux-64 gxx_linux-64 gsl numpy Cython scipy jupyter ipython h5py matplotlib python=3.8
conda activate gbgpu_env

If on MACOSX, substitute `gcc_linux-64` and `gxx_linus-64` with `clang_osx-64` and `clangxx_osx-64`.

2. Clone the repository.

git clone https://github.com/mikekatz04/GBGPU.git
cd GBGPU

3. Run install. Make sure CUDA is on your PATH if installing for GPU.

python setup.py install

4. To import gbgpu:

from gbgpu.gbgpu import GBGPU

To install this software for CPU usage, you need gsl >2.0, Python >3.4, and NumPy. We generally recommend installing everything, including gcc and g++ compilers, in the conda environment as is shown in the examples here. This generally helps avoid compilation and linking issues. If you use your own chosen compiler, you may need to add information to the setup.py file.

To install this software for use with NVIDIA GPUs (compute capability >2.0), you need the CUDA toolkit and CuPy. The CUDA toolkit must have cuda version >8.0. Be sure to properly install CuPy within the correct CUDA toolkit version. Make sure the nvcc binary is on $PATH or set it as the CUDAHOME environment variable.

0. Install Anaconda if you do not have it.

1. Create a virtual environment. Note: There is no available conda compiler for Windows. If you want to install for Windows, you will probably need to add libraries and include paths to the setup.py file.

conda create -n gbgpu_env -c conda-forge gcc_linux-64 gxx_linux-64 gsl numpy Cython scipy jupyter ipython h5py matplotlib python=3.8
conda activate gbgpu_env

If on MACOSX, substitute `gcc_linux-64` and `gxx_linus-64` with `clang_osx-64` and `clangxx_osx-64`.

2. If using GPUs, use pip to install cupy. If you have cuda version 9.2, for example:

pip install cupy-cuda92

3. Clone the repository.

git clone https://github.com/mikekatz04/GBGPU.git
cd GBGPU

4. Run install. Make sure CUDA is on your PATH.

python setup.py install

Change to the testing directory:

cd gbgpu/tests

Run in the terminal:

python -m unittest discover

We use SemVer for versioning. For the versions available, see the tags on this repository.

Current Version: 1.0.0

• Michael Katz
• Travis Robson
• Neil Cornish
• Tyson Littenberg
• Stas Babak

This project is licensed under the GNU License - see the LICENSE.md file for details.