this post was submitted on 14 Jul 2024
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Many of these have C-bindings for their libraries, which means that slowness is caused by bad code (such as making a for loop with a C-call for each iteration instead of once for the whole loop).
I am no coder, but it is my experience that bad code can be slow regardless of language used.
Bad code can certainly be part of it. The average skill level of those coding C/C++/Rust tends to be higher. And modern programs typically use hundreds of libraries, so even if your own code is immaculate, not all of your dependencies will be.
But there's other reasons, too:
And when working with multiple threads in Java, it is considered best practice to always clone memory of basically anything you touch. Like, that's good code and its performance will be mediocre. Also, you better don't think about using multiple threads in Python+JS. For those two, even parallelism was an afterthought.
Well, and then all of the above feeds back into all the libraries not being performant. There's no chance to use the languages for performance-critical stuff, so no one bothers optimizing the libraries.
Java is still significantly faster and more efficient than Python tho - because it has ahead-of-time optimizations and is not executing plain text.
Idk numpy go brrrrrrrrrr. I think it's more just the right tool for the right job. Most languages have areas they excel at, and areas where they're weaker, siloing yourself into one and thinking it's faster for every implementation seems short sighted.
At it's heart, numpy is C tho. That's exactly what I'm talking about. Python is amazing glue code. It makes this fast code more useful by wrapping it in simple(r) scripts and classes.