Why Python Is So Slow (And What Is Being Done About It)
Why Python Is So Slow (And What Is Being Done About It)
PyCon 2024 showcased a number of ways to speed the pokey Python programming language including sub-interpreters, immortal objects, just-in-time compilation and more.
When you need speed in Python, after profiling, checking for errors, and making damn sure you actually need it, you code the slow bit in C and call it.
When you need speed in C, after profiling, checking for errors, and making damn sure you actually need it, you code the slow bit in Assembly and call it.
When you need speed in Assembly, after profiling, checking for errors, and making damn sure you actually need it, you're screwed.
Which is not to say faster Python is unwelcome, just that IMO its focus is frameworking, prototyping or bashing out quick and perhaps dirty things that work, and that's a damn good thing.
Generally I bash together the one-off programs in Python and if I discover that my "one off" program is actually being run 4 times a week, that's when I look at switching to a compiled language.
Case in point: I threw together a python program that followed a trajectory in a point cloud and erased a box around the trajectory. Found a python point cloud library, swore at my code (and the library code) for a few hours, tidied up a few point clouds with it, job done.
And then other people in my company also needed to do the same thing and after a few months of occasional use, I rewrote it using C++ and Open3D. A few days of swearing this time (mainly because my C++ is a bit rusty, and Open3D's C++ interface is a sparsely-documented back end to their main python front end).
End result though is that point clouds that took 3 minutes to process before in python now take 10 seconds, and now there's a visualisation widget that shows the effects of the processing so you don't have to open the cloud in another viewer to see that it was ok.
But anyway, like you said, python is good for prototyping, and when you hash out your approach and things are fairly nailed down and now you'd like some speed, jump to a compiled language and reap the benefits.
And at that point you’ll also have a better idea of the problem and solution.
Python is also pretty good for production, provided you're using libraries optimized in something faster. Is there a reason you didn't use Open3D's Python library? I'm guessing you'd get close to the same performance of the C++ code in a lot less time.
That said, if you're doing an animation in 3D, you should probably consider a game engine. Godot w/ GDScript would probably be good enough, though you'd spend a few days learning the engine (but the next project would be way faster).
If you're writing a performance-critical library, something compiled is often the better choice. If you're just plugging libraries together, something like Python is probably a better use of your time since the vast majority of CPU time can generally be done in a library.
While I agree with most of what you say, I have a personal anecdote that highlights the importance of performance as a feature.
I have a friend that studies economics and uses python for his day to day. Since computer science is not his domain, he finds it difficult to optimize his code, and learning a new language (C in this case) is not really an option.
Some of his experiments take days to run, and this is becoming a major bottleneck in his workflow. Being able to write faster code without relying on C is going to have a significant impact on his research.
Of course, there are other ways to achieve similar results, for example another friend is working on DIAS a framework that optimizes pandas in the runtime. But, the point still stands, there are a tonne of researchers relying on python to get quick and dirty results, and performance plays a significant in that when the load of data is huge.
Sure, I was being mildly facetious, but pointing to a better pattern, the nature of python means it is, barring some extreme development, always going to be an order of magnitude slower than compiled. If you're not going to write even a little C, then you need to look for already written C / FORTRAN / (SQL for data) / whatever that you can adapt to reap those benefits. Perhaps a general understanding of C and a good knowledge of what your Python is doing is enough to get a usable result from a LLM.
I have an alternative anecdote.
My coworker is a Ph.D in something domain specific, and he wrote an app to do some complex simulation. The simulation worked well on small inputs (like 10), but took minutes on larger inputs (~100), and we want to support very large inputs (1000+) but the program would get killed with out of memory errors.
I (CS background) looked over the code and pointed out two issues:
- bubble sort in a hot path
- allocated all working memory at the start and used 4D arrays, when 3D arrays and a 1D result array would've sufficed (O(n4) -> O(n3))
Both problems would have been solved had they used Python, but they used Fortran because "fast," but it doesn't have builtin sort or data structures. Python provides classes, sortable lists (with quicksort!), etc, so they could've structured their code better and avoided the architectural mistakes that caused runtime and memory to explode. Had they done that, I could've solved performance problems by switching lists to numpy arrays and throwing numba on the hot loops and been done in a day, but instead we spent weeks rewriting it (nobody understands Fortran, and that apparently included the original dev).
Python lets you focus on the architecture. Compiled languages often get you stuck in the weeds, especially if you don't have a strong CS background and just hack things until it works.
I'd really like to see Rust fit in where C(++) does now for Python. I know some libraties do it (e.g. Pydantic), but it really should be more common. It should work really well with the GIL... (or the TIL or whatever the new one is)
In all the stuff I do in Python, runtime is not a consideration at all. Developer productivity is far more of a bottleneck. Having said that, I do of course see the value in these endeavours.
If everyone had a magic lamp that told them whether performance was going to be an issue when they started a project then maybe it wouldn't matter. But in my experience people start Python projects with "performance doesn't matter", write 100k lines of code and then ask "ok it's too slow now, what do we do". To which the answer is "you fucked up, you shouldn't have used Python".
Why on earth would we try to make snakes faster? Science has gone too far this time. What's next, give them arms?
Wings.
The aim is to offer the speed of C or C++ while retaining the user-friendly feel of Python itself.
These kind of claims always annoy me. Like, sure, there's some room for interpretation there, but at the end of the day, C, C++ and also Rust achieve their speed by having handling baked into the semantics for:
- non-GC memory management
- passing by-reference vs. by-value
- and in the case of Rust, also for handling multi-threaded processing.
Unless he comes up with a revolutionary new memory management strategy, or achieves a massive jump in static analysis to replace human intelligence, then you simply can't achieve similar speed while keeping the semantics of Python.
That's not really true. C# and Java are reference-based, uses GC and can be multithreaded, and are very comparable to Rust/C++/C performance. Certainly no more than twice as bad. Whereas Python is probably 50x as bad.
The real answer is that Python developers have deliberately avoided worrying about performance when designing the language, until maybe 2 years ago. That means it has ended up being extremely dynamic and difficult to optimise, and the CPython implementation itself has also not focused on performance so it isn't fast.
But I agree the aim of offering C/C++ speed is never going to be met with Python syntax.
They can probably beat or at least match Javascript, which has been heavily optimized, but the cap is going to be something like Lua (not LuaJit) without significant, painful changes.
If you want faster Python today, you can try numba or Cython, both solve the problem in a different way with different tradeoffs.
What/who wrote this article? There seem to be extra words here and there
Jimmy Two Times, from Goodfellas
http://3.bp.blogspot.com/-I820hY8aMco/T38ZpnH74WI/AAAAAAAAAWY/cKNXbeQ1-YQ/s1600/JimmyTwoTimes.jpeg
There's also: https://www.modular.com/max/mojo
