But then writing something in C is not really difficult isnt it? (The real difficult part is the algorithm not the implementation in a language)
[1] https://github.com/JuliaLang/Microbenchmarks/blob/af3d18f7b3...
[2] https://github.com/JuliaLang/Microbenchmarks/blob/af3d18f7b3...
Most the speed advantage of the C here is due to reusing the same memory over and over, which you can do pretty easily in Julia as well. Here's the Julia code using in-place operations and it's still quite a bit more readable than the C version: https://gist.github.com/StefanKarpinski/e57f5a36b7890b261a0d.... I timed this and it's the same speed as the C version. When developing this, it follows the same general outline as the C version, but you have several benefits: (1) You can use asserts to compare to the easy version; (2) There are niceties like bounds checks and array indexing. In C you can't do (1) because there is on easy version to compare with. And doing the array index computations in C is kind of a nightmare—it's so easy to accidentally screw them up.
Julia hasn't been around long enough to build ecosystem with multiple commerical giants building competing products.
> Julia does not make that sacrifice.
Sure, Julia sacrifices your sanity on the alter of stack traces, method resolution, time to first plot, among others.
> fairly limited subset of python
Fantastic comment, always brought up, except that this same subset of Python (really how to use CPU efficiently) is about the same that anyone writing about Julia performance is preaching.
Julia provides the interoperability between such libraries, including custom datatypes, frequently with fast zero-cost abstractions.
> fast zero-cost abstraction
Until you get a compiler error and the stack trace takes up a few screens of text. Who maintains that abstraction by the way? Nothing is free.
This is completely and demonstrably false. Julia allows fast programming with macros, multiple dispatch, abstract types and more.
In fact, those aren't special features and there is no other subset. Those are the core abstractions on which everything in Julia, down to primitive types and wrappers for llvm intrinsics are built. Without them you wouldn't have Julia.
Julia's GPU ecosystem wouldn't be where it is with just one or two people maintaining it without being able to reuse those features to plug into existing machinery.
In fact, multiple dispatch is essential to get good performance, abstract types have a cost if explicitly used inside struct definitions, but that's why you use parametric types, and abstract types do not have a cost in function signatures.
Macros are expanded at compile-time, and at least have no runtime cost, in fact they are often used for improved performance.
I think there's some fundamental misunderstanding going on here, but I'm not sure what it is. Or do you mean to say that if it is possible to write slow code in a language, then only a subset of that language has good performance? If that is the case, I don't know how to respond.
Jax is composable. In fact it’s a core design goal. Jax arrays implement the Numpy API. I routinely drop Jax arrays into other python libraries designed for Numpy. It works quite well. It’s not effortless 100% of the time but no library interop is (including Julia multiple dispatch).
I can introspect Jax. Until you wrap your function with jit(foo) it’s as introspectable as any other Python code, at least if I’m understanding what you mean by introspection.
Jax has implemented most of the Numpy functions, certainly most of the ones anyone needs to use on a regular basis. I rarely find anything missing. And if it is, I can write it myself, in python, and have it work seamlessly with the rest of Jax (autodiff, jit, etc)
You want to work with Units or track Measurment error (or both?). Basically same story. Except better in some ways worse in others. Better because you don't have to fork numpy, it is extensible enough to allow that. Packages exist that use that etendability for exactly that. Worse because those are scalar types, why are you even having to write code to deal with array support at all. Agian 2 julia packages and they don't even mention arrays internally.
The problem's not Jax. The problem is numpy. Or rather the problem is this level of composability is really hard most of the time in most languages (including the python + C combo. Especially so even).
Its true that this is not always trivial 100$% of the time with julia's multiple dispatch. but it is truer there than anywhere else i have seen.
Makes me think of this little pun: https://github.com/FluxML/Flux.jl/blob/master/src/utils.jl#L...
Does this work with numba? Are your jit-compiled functions compiled into external modules?