Pyccel's main goal is to resolve the principal bottleneck in scientific computing: the transition from prototype to production. Programmers usually develop their prototype code in a user-friendly interactive language like Python, but their final application requires an HPC implementation and therefore a new production code. In most cases this is written in a statically compiled language like Fortran/C/C++, and it uses SIMD vectorization, parallel multi-threading, MPI parallelization, GPU offloading, etc.
Sounds interesting!
In most cases figuring out how to do it with nutty numpy is the least time consuming and is fast enough.
Pyston I was able to get a 15% increase in speed out of the box. PyPy I could not get to work with my project (numpy/pandas/ta-lib), I finally got everything to compile but it blew through my 32 gigs in 10 seconds and crashed.
Pyccel, Cython, Numba and Rapids I have yet to try but am interested in anyones experience.
ultimately, i'd probably look at julia or maybe rust if i was building something from scratch these days though. i'll also note that adding numba to a project is a pretty heavyweight proposition (you are, afterall, adding llvm to your project).
