Nowadays, many people use CUDA with Python. It works not only as a glue of binaries, but it also enables to us write GPU accelerated code directly. As a glue language, Python can call the APIs from the CUDA C/C++ libraries, using pybind11 (https://github.com/pybind/pybind11) or SWIG (http://swig.org/). However, we have to write CUDA C/C++ codes and integrate them into the Python application.
However, there are Python packages—Numba, CuPy, and PyCUDA—that enable GPU programming with Python. They provide native accelerated APIs and wrappers for CUDA kernels. In other words, we don't have to write C/C++ code and spend our time performing integration. Numba provides a vectorization and CUDA just-in-time (jit) compiler to accelerate its operation. It is compatible with NumPy, so you can accelerate...