Even when the 'spawn' strategy is used (default on Windows, and can be chosen explicitly on Linux), the overhead can largely be avoided. (Why choose it on Linux? Apparently forking can cause problems if you also use threads.) Python imports can be deferred (`import` is a statement, not a compiler or pre-processor directive), and child processes (regardless of the creation strategy) name the main module as `__mp_main__` rather than `__main__`, allowing the programmer to distinguish. (Being able to distinguish is of course necessary here, to avoid making a fork bomb - since the top-level code runs automatically and `if __name__ == '__main__':` is normally top-level code.)

But also keep in mind that cleanup for a Python process also takes time, which is harder to trace.

Refs:

https://docs.python.org/3/library/multiprocessing.html#conte... https://stackoverflow.com/questions/72497140

I really wish Python had a way to annotate things you don't care about cleaning up. I don't know what the API would look like, but I imagine something like:

  l = list(cleanup=False)
  for i in range(1_000_000_000): l.append(i)

telling the runtime that we don't need to individually GC each of those tiny objects and just let the OS's process model free the whole thing at once.

Sure, close TCP connections before you kill the whole thing. I couldn't care less about most objects, though.

Tbh if you're optimizing python code you've already lost

On a 64-core machine, Python code that uses all the cores will be modestly faster than single-threaded C, even if all the inner loops are in Python. If you can move the inner loops to C, for example with Numpy, you can do much better still. (Python is still harder to get right than something like C or OCaml, of course, especially for larger programs, but often the smaller amount of code and quicker feedback loop can compensate for that.)

I strongly doubt this claim. Python is more than 64x slower than C without synchronization overhead in most numeric tasks, with synchronization overhead on those processes it should be much worse.

Python is so much slower than any native or JIT compiled language that it begets things like numpy in the first place.

My typical experience is about 40×.

Run along.

You'd presumably need to do something involving weakrefs, since it would be really bad if you told Python that the elements can be GCd at all (never mind whether it can be done all at once) but someone else had a reference.

Or completely rearchitect the language to have a model of automatic (in the C sense) allocation. I can't see that ever happening.

I don't think either of those are true. I'm not arguing against cleaning up objects during the normal runtime. What I'd like is something that would avoid GC'ing objects one-at-a-time at program shutdown.

I've had cases where it took Python like 30 seconds to exit after I'd slurped a large CSV with a zillion rows into RAM. At that time, I'd dreamed of a way to tell Python not to bother free()ing any of that, just exit() and let Linux unmap RAM all at once. If you think about it, there probably aren't that many resources you actually care about individually freeing on exit. I'm certain somewill will prove me wrong, but at a first pass, objects that don't define __del__ or __exit__ probably don't care how you destroy them.

Ah.

I imagine the problem is that `__del__` could be monkeypatched, so Python doesn't strictly know what needs custom finalization until that moment.

But if you have a concrete proposal, it's likely worth shopping around at https://discuss.python.org/c/ideas/6 or https://github.com/python/cpython/issues/ .

I might do that. It’s nothing I’ve thought about in depth, just an occasionally recurring idea that bugs me every now and then.

Never experienced this. If this is truly a problem, here is a sledgehammer, just beware it will not close your tcp connections gracefully: os.kill(os.getpid(), signal.SIGKILL).

There's already a global:

  import gc
  gc.disable()

So I imagine putting more in there to remove objects from the tracking.

That can go a long way, so long as you remember to manually GC the handful of things you do care about.

Is there a good way to add __del__() methods or to wrap Context Manager __enter__()/__exit__() methods around objects that never needed them because of the gc?

Hadn't seen this:

  import gc
  gc.disable()

Cython has __dealloc__() instead of __del__()?

Also, there's a recent proposal to add explicit resource management to JS: "JavaScript's New Superpower: Explicit Resource Management" https://news.ycombinator.com/item?id=44012227

And then we're back to manual memory management.

At least the objects get instantiated automatically, and you don't need to malloc() them into existence yourself; I guess that's still something.

> Which is why, at least on Linux, Python's multiprocessing doesn't do that but fork()s the interpreter

…which can also be a great source of subtle bugs if you're writing a cross-platform application.