Installation (GPU)


Python users may install the cuFINUFFT package using pip install cufinufft, which contains binary wheels compiled against CUDA 10.2 on Linux. If these requirements do not work for your use case, please see the detailed instructions below.

The GPU version of FINUFFT is called cuFINUFFT, and it uses CUDA kernels (often exploiting fast GPU shared memory) to speed up spreading/interpolation operations, as well as cuFFT. It operates on GPU arrays, which enables low-overhead integration with other GPU processing pipelines, but does requires the user to transfer their data between the host (CPU) and the device (GPU). See the main overview page and reference [S21] for more details. It is currently being tested on the Linux platform, but you should be able to adapt the instructions below to work on other platforms, such as Windows and macOS.

CMake installation

To automate the installation process, we use cmake. To use this, run

mkdir build
cd build
cmake --build .

The (along with will now be present in your build directory. Note that for this to work, you must have the Nvidia CUDA toolchain installed (such as the nvcc compiler, among others). To speed up the compilation, you could replace the last command by cmake --build . -j to use all threads, or cmake --build . -j8 to specify using 8 threads, for example. To avoid building the CPU library (, you can set the FINUFFT_USE_CPU flag to OFF.

In order to configure cuFINUFFT for a specific compute capability, use the CMAKE_CUDA_ARCHITECTURES flag. For example, to compile for compute capability 8.0 (supported by Nvidia A100), replace the 3rd command above by


To find out your own device’s compute capability without having to look it up on the web, use:

nvidia-smi --query-gpu=compute_cap --format=csv,noheader

This will return a text string such as 8.6 which would incidate sm_86 architecture, thus to use CMAKE_CUDA_ARCHITECTURES=86.


To test your cuFINUFFT package, configure it with the BUILD_TESTING and FINUFFT_BUILD_TESTS flags set to ON. In other words, run


Then after compiling as above with cmake --build ., you execute the tests using

cmake --build . -t test

This runs a suite of GPU accuracy (mathematical correctness) and interface API tests. See the test/cuda/ directory for individual usage and documentation of these tests.

Python interface

In addition to the C interface, cuFINUFFT also comes with a Python interface. As mentioned above, this can be most easily installed by running pip install cufinufft, but it can also be installed from source. The Python interface code is located in the python/cufinufft subdirectory, so to install it, you first build the shared library as seen above, then run

pip install python/cufinufft

Note that since cuFINUFFT supports a number of different GPU frameworks (CuPy, Numba, PyTorch, and PyCuda), it does not install any of these automatically as a dependency. You must therefore install one of these manually. For example, for CuPy, you would run

pip install cupy-cuda11x

for the CUDA 11.2–11.x version of CuPy. Assuming pytest is installed (otherwise, just run pip install pytest), you can now test the installation by running

pytest --framework=cupy python/cufinufft/tests

In contrast to the C interface tests, these check for correctness, so a successful test run signifies that the library is working correctly. Note that you can specify other framework (pycuda, torch, or numba) for testing using the --framework argument.