[blt]

Surprised Python is only 21x slower than C for tree traversal stuff. In my experience that's one of the most painful places to use Python. But maybe that's because I use numpy automatically when simple arrays are involved, and there's no easy path for trees.

[tweakimp]

Be careful with that, numpy arrays can be slower than Python tuples for some operations. The creation is always slower and the overhead has to be worth it.

[__rito__]

Yeah. Many seem to forget it. For one-off computation tasks, NumPy, PyTorch, JAX have non-trivial overhead, and might even be slower than vanilla Python. Only when repetition, loops, etc. come into the picture, which is recurring in many people’s workflow - JAX or NumPy is worth it.

[blt]

yes of course, in my field the arrays are large and we're doing nontrivial work per element.

[AlotOfReading]

You can turn trees into numpy-style matrix operations because graphs and matrices are two sides of the same coin. I don't see the code for the binary-tree benchmark in the repo to see how it's written, but there are libraries like graphblas that use the equivalence for optimization.