Installation guide

If GPU acceleration is required, make sure the CUDA toolkit is installed first.

The C library

The AccRQA library is compiled from source using CMake.

From the top-level directory, run the following commands to compile and install the library:

mkdir build
cd build
cmake .. [OPTIONS]
make
make install

To specify the CUDA architecture use -DCUDA_ARCH="x.y" flag, where x and y are major and minor compute capability on NVIDIA GPU. For example, for architecture 7.0, do

cmake -DCUDA_ARCH="7.0" ../

To compile binding to R, you have to specify R library location using -DCMAKE_R_LIB_DIR and location of the R include directory using -DCMAKE_R_INC_DIR. For example

cmake -DCMAKE_R_INC_DIR="/usr/share/R/include" -DCMAKE_R_LIB_DIR="/usr/lib/R/lib/libR.so"  ../

The Python library

From the top-level directory, run the following commands to install the Python package:

pip3 install .

The compiled library will be built as part of this step, so it does not need to be installed separately. If extra CMake arguments need to be specified, set the environment variable CMAKE_ARGS first, for example:

CMAKE_ARGS="-DCUDA_ARCH=7.0" pip3 install .

Uninstalling

The Python package can be uninstalled using:

pip3 uninstall accrqa