I'm pretty good at Python and have some basic knowledge of C, but I'm getting really frustrated with the speed limitations of Python. I've started using Python's multiprocessing, but I keep running into mysterious overhead issues that make me think I might need to learn another language to solve my performance problems. I'm a bit intimidated by Rust, as everyone I know finds it hard to master, although I like the concept. I mostly write scientific code, so I'd love to get some recommendations on languages that are both fast and allow for easy parallelization. What do you think I should go for?
5 Answers
Have you thought about Fortran? It's been around forever and a lot of time has gone into optimizing it for parallelism. Plus, if you publish a scientific paper using Fortran, people will respect it! That's a huge advantage.
I’d suggest giving Go a try. It's got a syntax that's pretty similar to C and has great built-in support for parallelism. Plus, many folks find it easier to pick up than Rust. Seriously, it's worth considering!
Definitely Go! The easy syntax and its focus on concurrency mean you can write parallelized code without much hassle, and it's fast enough for most tasks.
Have you considered using Elixir? While it's not the typical choice for CPU-intensive tasks, its functional programming model makes it great for parallel programming. If you're willing to venture outside of mainstream languages, it could be a solid option!
Don't overlook using optimized Python! Libraries like Numba can really boost your code's performance for heavy computations. If that still isn’t cutting it, then you might be ready to explore a different language, but sometimes, diving deeper into Python’s optimization possibilities is all you need.
Yeah! PyNumba is awesome, especially for the loops that can't be vectorized; just keep in mind that optimizing your Python can lead to a speed boost without switching languages.
Rust might seem tough at first, but it’s really beneficial for writing efficient parallel code. You get to learn a lot about memory management, which is crucial for optimization. Plus, it works great with Python, so you can implement Rust in parts of your project where performance really counts.
I totally get that fear of Rust; it can feel overwhelming. But once you get past the initial learning curve, it really pays off, especially with its tooling for performance.
And don't forget, the math libraries in Fortran are top-notch; they've been optimized over decades.