RI Kernels
CPU and GPU kernels for radio interferometry, exposed to JAX as FFI custom calls with full support for automatic differentiation.
Installation
The CPU kernels and all of the Python code live in ri_kernels. The GPU kernels
ship as add-on packages, one per CUDA major version, selected with an extra:
pip install ri_kernels # CPU kernels only
pip install "ri_kernels[cuda12]" # + CUDA 12 kernels and jax[cuda12]
pip install "ri_kernels[cuda13]" # + CUDA 13 kernels and jax[cuda13]
Building from source
Requires CMake >= 3.20, a C++20 compiler, and network access at configure time (Google Highway is fetched by CMake).
pip install . # CPU only
RI_KERNELS_CUDA=1 pip install . # CPU + CUDA, needs nvcc
RI_KERNELS_ROCM=1 pip install . # CPU + ROCm, needs hipcc
Both libraries land in the ri_kernels package directory in that case, which is
also a location the loader searches. Note that pip install .[cuda12] is a
different thing: the extra resolves ri_kernels_cuda12 from the index, so it
needs a published release. To build the GPU kernels from the checkout, use
RI_KERNELS_CUDA=1.
Other CMake options of note: RI_KERNELS_CPU (default ON),
RI_KERNELS_MULTI_ARCH (dynamic SIMD dispatch, default ON — turn it off and
set arch flags via CMAKE_CXX_FLAGS for a single-target build),
RI_KERNELS_BUNDLED_HIGHWAY, CMAKE_CUDA_ARCHITECTURES, and
RI_KERNELS_STATIC_CUDART (default ON; set it to OFF when a package
manager should provide the shared CUDA runtime).
Tests
Tests can be run with
python -m pytest tests
2 maintainers
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