Since MatX was originally intended for streaming/real-time processing the focus has been on making C++ for CUDA easier to use for those kinds of applications.
There are also a lot of things in cuPy and sciPy that don't make a lot of sense to do on the GPU, like offline tasks such as filter design in signal processing.
Also, since C++ users typically are writing in that language for maximum performance, we've put a large focus on making sure we are as close to writing optimized CUDA as possible. In general, most workloads we've tested are about 3-4x faster than the cuPy counterpart due to better fusion and language overhead.
We have discussed supporting cusparse as well, but cusparse typically requires a different input type like a CSR matrix. This is not something you'd typically want to detect/convert, so we're still discussing ways of getting this integrated cleanly.
We do have several versions of SVD, including one that calls cuSolver.
Feedback is always appreciated!
Although I'm not expecting on that scale here, but what's the planned future work here? E.g. matrix free operators and operations, sparse matrix support, or symmetry aware eigenvalue / singular value decompositions? There are official cuda libraries that support these (e.g. cuSPARSE).
Otherwise this looks good! Documentation is decent too.