Thanks for this.
Will Julia advocates every use honest benchmarks to make their language look good? I doubt it.
Will Julia advocates every use honest benchmarks to make their language look good? I doubt it.
Even if it doesn't say that, bashing people who use Julia for a repository made by a Python user is a new level of HN trolling.
According to OP, he tried this but it resulted in slower execution for him. I'm not sure he really followed what that PR says. In the case of the submitter, it says on their machine it gave a 2x speedup (smaller than my ~7x, but still significant).
Like OP, I observed that static typing make_list yields little benefit. It's runtime is 3x the runtime of iterate_list. And that makes sense. I'm not sure why we'd expect Cython to speed up the allocation of large numbers of Python objects.
Another funny thing: making a pure python version that uses the sum() built-in results in code that runs 80% as fast as the cython version, but also gives lower error (e-8 versus e-4 error that gets accumulated when the numbers are summed up one by one).
Namely, realizing these claims requires learning the language, and actually taking advantage of it's strengths rather than just writing 'python in julia'.
It's very common for Python users to show up on the Discourse forum and complain that julia is slower than python and then show some code that's basically just Python code written in julia, including a huge proliferation of global variables, allocating huge amounts of temporary arrays, etc.
There's also a huge spectrum of 'benchmark quality' out there. E.g. the benchmarks this HN post features seem pretty shitty and are not measuring anything interesting or useful as far as I can tell.