Comment by StefanKarpinski | Hacker News Reader

Comment by StefanKarpinski | Hacker News Reader

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StefanKarpinski

In TensorFlow one doesn't write Python code and run it on a G/TPU, one writes Python code that uses an API to construct a TensorFlow graph computation which is compiled and runs on the G/TPU. It's really a completely separate programming language with different semantics and a totally separate runtime hiding behind a Python API. This is why you can't use any of Python's normal libraries, you have to use TensorFlow libraries.

"Julia runs on GPUs" actually means that you write Julia code and it is compiled to natively run on GPUs [1]. Similarly, this post is about writing Julia code and compiling it to run natively on TPUs, not calling some predefined TPU library. Yes, of course you _can_ call libraries that are compiled for GPUs and TPUs—as you can in Python or any other language—but in Julia, nearly arbitrary user code can be compiled and run on GPUs and TPUs. The major restriction on GPUs is that you cannot do dynamic allocation, so you need to write non-allocating Julia code, but that's common requirement for high-performance code anyway.

[1] https://github.com/JuliaGPU/CUDAnative.jl

This is treading a fine line here, since you don’t mention Numba, which applies the same approach as Julia, namely translating the language to LLVM IR for generating PTX.

The same applies to those OpenACC pragmas that can offload a butt ugly Fortran loop to GPU: no one says Fortran is running on the GPU, rather the compiler is doing code gen and RT calls to make user life easy.

It thus smells like marketing rhetoric.

Numba compiling Python to PTX is absolutely "Python running on GPUs" and OpenACC is also "Fortran running on GPUs". If user code written in language X is compiled to target hardware Y then that is "X running on Y". This is fairly standard compiler terminology, not marketing speak. You specifically mentioned TensorFlow, which is NOT Python running on G/TPUs since the code that has neither Python's semantics nor its runtime.

This has been available for Python going waay back to PyCuda, and also numba has support for jit compiling to a GPU target. You can do similar things with Cython as well.

Except for numba (when used on a specific subset of python), none of these utilities let you use a pure-python function with them. You can not write a generic python algorithm unaware of these tools and expect it to work efficiently with any of them without rewriting.

Totally false. Cython allows you to choose whatever subset of the CPython API you want to use, to suit whatever performance tradeoffs you’d like to make in exchange for access to convenience, dynamic attribute access, whatever.

You can write fully no-python C functions, all the way up to straight CPython dynamically typed code relying on the GIL and reference counting, etc.

I think you are missing the point. How do I get a pure-python function written by someone else to be "C level" fast when used in the inner loop of your otherwise superfast cython? How do you do that without rewriting it in cython?

Sure, cython is great if you decide to rewrite the library you want to use in cython. Same with scipy.optimize for instance: it can be super fast for everything but ordinary python cost functions.

Here is an easy way to prove me wrong: Find the minimum of a pure python function quickly, with whatever python tool you want, without having to deal with the enormous python function call overhead, without rewriting the function. Imagine this function is deep down in another library and it uses a bunch of 3rd party libraries.

I think you have a very fundamental misunderstanding here.

When you ask for a pure Python loop, for example, you are directly saying you want something like the iterator protocol of Python, inclusive of its overhead, because your use case needs the dynanic behaviors, overloading via custom iterators, whatever. You’re directly saying the performance trade-off is worth it for you personally because a low overhead C-style loop won’t give you the extra features of e.g. the iterator protocol that matters more to you.

When you say, but my use case doesn’t need the iterator protocol, then you just write that piece of code in Cython or use a nopython numba jit call, etc., because you want to make your trade-off differently in that single case.

You’re essentially saying, “how do I make a triangle with 5 sides,” by requesting a pure Python section of code to not be pure Python.

On a side note though, you can take pure Python code and compile it directly with Cython, with no modifications, and in many cases it will still be quite faster because it can compile away some types of attribute accesses, overhead of boolean logical checks as function calls, and also reduce function call overhead for many standard library functions or data structures that are already implemented as C extension modules (by bypassing some of the method lookup logic to avoid checks on PyFunction objects in CPython).

Usually this is a recommended first step before ever adding a type annotation or doing anything more difficult.

Finally, just to be clear, numba (and tools using llvmlite more generally) don’t worry about this, since they perform static type inference and have other rules about enforcing static types when jitting a Python code segment.

"Here is an easy way to prove me wrong: Find the minimum of a pure python function quickly, with whatever python tool you want, without having to deal with the enormous python function call overhead, without rewriting the function. Imagine this function is deep down in another library and it uses a bunch of 3rd party libraries."

Have you tried that yet?

That question is bizarre and just points to more serious confusion on the part of the commenter above.

If you’re writing a general purpose function minimizer, you have to make assumptions about the functions you will minimize, for example that they are bounded below or that minimization is restricted to a closed subset of the domain.

If they rely on third party libraries, you have to make even more assumptions, that those third party function calls don’t require (for legit reasons) some Python-specific dynamic typing feature of the language that would imply that jit compiling them is impossible.

If you are willing to make these assumptions, then solving this is trivial: just monkeypatch all the relevant functions with jitted versions of the functions. You could even write a tool to walk the relevant packages with pkgutil and monkeypatch everything. Zero rewrites, and you wouldn’t even have to manually indicate what to monkeypatch.

For example the library gevent does this “monkeypatch everything” pattern to change all functions in the requests package into non-blocking async requests automatically.

If you are asking instead to preserve pure Python features that these third party libraries are using, like reliance on iterator protocol, descriptor protocol, metaclasses, context managers, Python data model special methods, dynamic attribute lookups, etc., then the question is once again not logically consistent. It’s asking for a triangle with 5 sides.

"these third party libraries are using, like reliance on iterator protocol, descriptor protocol, metaclasses, context managers, Python data model special methods, dynamic attribute lookups, etc.,"

Julia can Jit code that relies on vastly improved versions of all those features (minus some setatr etc).

Can you use them on a custom array type in any case let alone scipy? No. and that's the point. In julia, the optim package for example takes in abstract arrays with all those features. Mathematical assumptions are categorically different than unavailable or slow PL semantics.

Julia preserves full general programming language semantics including zero cost abstractions, higher order functions, closures, zero cost differentiation and extending custom types, while still being fast enough for numerical computing (minus exceptions).

It seems to be that Python is sticking with 1d figures and that's assuming your monkey patch idea works, also ignoring the ecosystem cost and the fact that monkeypatching in Julia is part of normal code design (through multimethods) whereas in python it's a code smell.

I think you are really grasping at straws here.

> “Julia can Jit code that relies on vastly improved versions of all those features (minus some setatr etc).”

This basically summarizes the problem with most Julia upvote party posts like this one on Hacker News. Your comment is totally one-sided, Julia is better at every possible thing, so much that you are noseblind to it and can’t get an outside perspective that no, in fact, Julia’s language features do not have some fully dominating feature by feature parity compared against Python.

Every time it’s just an agonizing dragged out comment thread full of this type of overly one-sided thinking. Usually I just ignore all Julia posts for exactly this reason, and probably should have this time too, but seeing Python jit options and Cython options misrepresented so badly just got the better of me.

You are using now denigrating comments instead of answering simple questions. You are not even explaining what is wrong with the quote you took from the previous comment. The poster never said "Julia is better at every possible thing", but you are totally saying that about python by pretending it is not a chore and a difficult learned skill to write fast python numerics.

Yes, we all know that if you program in a very particular way (basically by not using any of the great dynamic or introspective features) you get fast python. How is it not objectively better to have a language that is fast independently of whether you use its dynamic/introspective/metaprogramming features?

"Python is fast as long as I program in this very particular and very constrained way" is a silly way to defend python (which is nonetheless an amazing language).

Julia has a ton of "zero cost abstractions". Python, as great as it is, simply does not.

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