gbgpu: GPU/CPU Galactic Binary Waveforms
This package generates Galactic binary waveforms. It is based on code (FastGB) by Neil Cornish, Tyson Littenberg, Travis Robson, and Stas Babak.
Getting Started
Below is a quick set of instructions to get you started with gbgpu
.
Install Anaconda if you do not have it.
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 thesetup.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`.
Clone the repository.
git clone https://github.com/mikekatz04/GBGPU.git
cd GBGPU
Run install. Make sure CUDA is on your PATH if installing for GPU.
python setup.py install
To import gbgpu:
from gbgpu.gbgpu import GBGPU
Prerequisites
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.
Installing
Install Anaconda if you do not have it.
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 thesetup.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`.
If using GPUs, use pip to install cupy. If you have cuda version 9.2, for example:
pip install cupy-cuda92
Clone the repository.
git clone https://github.com/mikekatz04/GBGPU.git
cd GBGPU
Run install. Make sure CUDA is on your PATH.
python setup.py install
Running the Tests
In the main directory of the package run in the terminal:
python -m unittest gbgpu/tests/test_gbgpu.py
Versioning
We use SemVer for versioning. For the versions available, see the tags on this repository.
Current Version: 1.0.0
License
This project is licensed under the GNU License - see the LICENSE.md file for details.
Documentation: