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Wrestling Python into LLVM Intermediate Representation (2019) [video]

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Re: Wrestling Python into LLVM Intermediate Representation (2019) [video]

#4

Post by tony »

I'm not sure if it's an exact fit, but dropbox used to have pyston: https://github.com/dropbox/pyston

The blog posts by Kevin Modzelewski went into the internals: https://blog.pyston.org/

Also by the same person, a good article on Python's performance: http://blog.kevmod.com/2016/07/why-is-python-slow/

As a side comment: On the subject of type inference, I really come to like python's type hints. I don't use mypy itself yet, but already had a habit of adding types in docstrings, and like it when I can add a clue that a signature/return is dealing with a something that's not a basic type.

There are some nice little things out there for advanced typing, being added now and then:

- TypedDict and Literal: https://github.com/python/typing/blob/master/typing_extensio... (TypedDict was accepted in PEP 589[1])

- NamedTuple had variable annotation added in Python 3.6: https://www.python.org/dev/peps/pep-0526/

I wonder if typings are added to a codebase, we could give LLVM one more shot. It'd be pretty crazy to build able to build a large graphql server and build it into a statically linked binary like golang.

[1] https://www.python.org/dev/peps/pep-0589/

Re: Wrestling Python into LLVM Intermediate Representation (2019) [video]

#5

Post by pjmlp »

tony wrote: post #4

I'm not sure if it's an exact fit, but dropbox used to have pyston: https://github.com/dropbox/pyston The blog posts by Kevin Modzelewski went into the internals: https://blog.pyston.org/ Also by the same person, a good article on Python's performance: http://blog.kevmod.com/2016/07/why-is-python-slow/ As a side comment: On the subject of type inference, I really come to like python's type hints. I don't use mypy its…

It would already be nice if it was Julia LLVM JIT style.

Many of the "why Python is slow" articles usually hand wave the fact that other languages, just as dynamic, like Common Lisp and Smalltalk, have quite capable JIT compilers.

So it is a matter of having enough resources to throw at it.

Maybe PyPy and Numba are as good as it gets, unless some big corporation is willing to spend big bucks into improving Python's JITs.

Re: Wrestling Python into LLVM Intermediate Representation (2019) [video]

#6

Post by marmaduke »

pjmlp wrote: post #5
tony wrote: post #4

I'm not sure if it's an exact fit, but dropbox used to have pyston: https://github.com/dropbox/pyston The blog posts by Kevin Modzelewski went into the internals: https://blog.pyston.org/ Also by the same person, a good article on Python's performance: http://blog.kevmod.com/2016/07/why-is-python-slow/ As a side comment: On the subject of type inference, I really come to like python's type hints. I don't use mypy its…

It would already be nice if it was Julia LLVM JIT style. Many of the "why Python is slow" articles usually hand wave the fact that other languages, just as dynamic, like Common Lisp and Smalltalk, have quite capable JIT compilers. So it is a matter of having enough resources to throw at it. Maybe PyPy and Numba are as good as it gets, unless some big corporation is willing to spend big bucks into improving Python's J…

> unless some big corporation is willing to spend big bucks

It seems like the outcome of the Unladen Swallow project was that there was no point to JIT generally because the language semantics prevent the sort of accelerations possible in other languages. (I'm aware of how PyPy and Numba are successful exceptions.)

Re: Wrestling Python into LLVM Intermediate Representation (2019) [video]

#8

Post by jashmatthews »

marmaduke wrote: post #6
pjmlp wrote: post #5

Earlier quoted context omitted.

It would already be nice if it was Julia LLVM JIT style. Many of the "why Python is slow" articles usually hand wave the fact that other languages, just as dynamic, like Common Lisp and Smalltalk, have quite capable JIT compilers. So it is a matter of having enough resources to throw at it. Maybe PyPy and Numba are as good as it gets, unless some big corporation is willing to spend big bucks into improving Python's J…

> unless some big corporation is willing to spend big bucks It seems like the outcome of the Unladen Swallow project was that there was no point to JIT generally because the language semantics prevent the sort of accelerations possible in other languages. (I'm aware of how PyPy and Numba are successful exceptions.)

LLVM just isn't that good for a JIT compiler:

http://qinsb.blogspot.com/2011/03/unladen-swallow-retrospect...

Azul made it work for Java with what they said was ~20 man years of work.

Re: Wrestling Python into LLVM Intermediate Representation (2019) [video]

#10

Post by heisig »

pjmlp wrote: post #5
tony wrote: post #4

I'm not sure if it's an exact fit, but dropbox used to have pyston: https://github.com/dropbox/pyston The blog posts by Kevin Modzelewski went into the internals: https://blog.pyston.org/ Also by the same person, a good article on Python's performance: http://blog.kevmod.com/2016/07/why-is-python-slow/ As a side comment: On the subject of type inference, I really come to like python's type hints. I don't use mypy its…

It would already be nice if it was Julia LLVM JIT style. Many of the "why Python is slow" articles usually hand wave the fact that other languages, just as dynamic, like Common Lisp and Smalltalk, have quite capable JIT compilers. So it is a matter of having enough resources to throw at it. Maybe PyPy and Numba are as good as it gets, unless some big corporation is willing to spend big bucks into improving Python's J…

Let me chip in here to clarify some things.

> other languages, just as dynamic, like Common Lisp and Smalltalk, have quite capable JIT compilers.

Common Lisp implementations don't need a JIT compiler to be fast. In fact, most of them don't use JIT compilation at all.

What makes Common Lisp implementations (especially SBCL) so fast is a combination of sophisticated static analysis, optional type declarations, and the fact that Common Lisp has been carefully designed to allow for high performance. I cannot stress how important the last point is. The Common Lisp standard is a contract between the programmer and the compiler writer that allows the former to write portable programs, yet gives the latter enough freedom to optimize.

In contrast, the language "standard" of Python doesn't clarify what portable programs may rely on. Instead, programmers tend to rely on the specific behavior of CPython. And reproducing the exact behavior of CPython is much harder than implementing a carefully designed standard.

> So it is a matter of having enough resources to throw at it.

No amount of resources can heal the design decisions of Python. The only way to get Python fast is by going through a painful standardization effort and by breaking some existing code. And I don't see that happening anytime soon. (Python 4 anyone?)

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