alt Hacker NewsThe line I'd push on is "valid Python that runs unmodified in CPython." True at the syntax level, but the speedup isn't really coming from Nim as a backend, it comes from how much of Python's dynamism you can pin down at compile time. Refcounting semantics, __getattr__, values that change type at runtime, isinstance-based dispatch, monkeypatching in tests: the moment you statically commit to any of those, you've defined a subset, and the subset is the actual product, not the transpiler.
I've spent a fair bit of time generating specialized straight-line code for hot Python paths, killing the per-call attribute and dict lookups the interpreter does. The lesson was that dispatch-bound code claws back most of its overhead without ever leaving CPython. Where AOT-to-native actually pulls ahead is numeric and loop-bound work, where the interpreter loop and boxing dominate. Your 512x288 render is exactly that case, which is why it looks so strong.
So the benchmark I'd want isn't render time, it's what fraction of a real module transpiles with no rewrites. That number tells me whether this is a systems language or a fast path I have to hand-shape around. Codon and Shedskin both hit that wall. Curious where Nimic draws the line.
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Curious why your comment below’s got flagged. I personally don’t see any issue with it.
True, nimic is a statically typed Python subset without much of its dynamism with the aim to be foremost an efficient systems language. Though in nimic, without using isinstance, the instance-based dispatch is realised via variant types. Yes, emulating the Nim constructions in Python was the hardest part, while making the transpiler was straightforward.
Indeed, the AOT compilation leads to great speed-ups for heavy custom numeric calculations that cannot be easily vectorized in numpy, such as the raytracing logic. In cases when most of calculations are performed in an external module (written in e.g. C, Rust or Cython etc) the performance gain might be much less, but the added value here is that the high performance module itself can be written in nimic, keeping the codebase purely in Python and consolidating the codebase.
When starting with a "pythonic" code, rewrites in nimic can be substantial but so would be a rewrite in a systems language like C or Rust. Besides allowing to optimise the fast path, nimic provides low level functionality, such as pointers to pointers and bitwise operations that actually executes within CPython, for example, as in mp4 muxer implementation in dima-quant/ndsl_raytracer/src/nraytracer/minimp4.py