. Of course systems like Numba change the Python performance game, which undermines D's potential in the Python-verse, as it does C and C++. Currently I am investigating Python/Numba/Chapel as the way of doing performance computing. Anyone who just uses Python/NumPy/SciPy is probably not doing performance computing, NumPy is so slow (*).
Can you elaborate ?
The issue here for me is that Chapel provides something that C, C++, D, Rust, Numba, NumPy, cannot – Partitioned Global Address Space (PGAS) programming. This directly attacks the multicore/multiprocessor/cluster side of computing, but not the GPGPU side, at least not per se.
What's the best reference to learn more about PGAS? Thanks. Laeeth.
