MatX is a C++20 numerical computing library targeting both CPUs and NVIDIA GPUs. It tries to use syntax familiar to both Python and MATLAB users.
MatX is a header-only library, but building examples, tests, and benchmarks requires a build environment with CMake, CUDA Toolkit, and a supported C++ host compiler.
The current versions required for each dependency are listed in @docs_input/build.rst. For a reproducible build environment, users can build the Docker container in
the @.devcontainer directory using the create_base_container.sh script. The python library hpccm is required to create the Dockerfile.
For use in external projects, MatX simply needs to be included by #include <matx.h>. To build unit tests the use the CMake option MATX_BUILD_TESTS=ON, benchmarks
MATX_BUILD_BENCHMARKS=ON, and examples MATX_BUILD_EXAMPLES=ON. Individual tests can be compiled separately via different targets output from CMake.
Every new public function, operator, transform, or backend path should include accompanying unit tests and documentation updates. Tests should cover the supported types, ranks, batching behavior, and important error or fallback cases for the new behavior. Documentation should describe how to use the feature, required build options or dependencies, and any limitations that would affect users or future maintainers. Any change to operators should also verify @docs_input/executor_compatibility.rst and update it if executor support, limitations, or backend requirements changed.
When adding or changing accelerated backends such as CUDA, cuBLAS, cuSolver, cuFFT, or MathDx paths, preserve the existing non-accelerated behavior unless the task explicitly calls for a breaking change. Prefer focused tests that compare the new backend against an existing trusted MatX path, and include negative tests for unsupported shape, dtype, rank, or configuration combinations when applicable.