Hi, I’m working with Pythran ( http://github.com/serge-sans-paille/pythran ), a Python/Numpy to C++ transpiler, which of course supports only a subset of Python (in particular not all the crazy cool Python stuffs stupid for performance). With Pythran, you can prototype in Python/Numpy and get very ef…

[image] stevengj: This was nearly a direct quotation. You wrote: About the limitation “to Python/Pythran/etc semantics”, I have to say that I don’t understand. Algorithms written in Python / Numpy are fully described. There is the same amount of information as for many other languages, including…

[image] Pierre_Augier: the result of such challenge could at least help the Julia community to explain why transpiling numerical kernels from Python / Numpy into effective Julia is not possible I don’t think anyone here is saying it is impossible; most of the responses so far have simply been…

[image] Pierre_Augier: I perfectly understand that Julia is a great piece of technology, and offers very interesting possibilities. This may cut to the heart of the matter: you seem to think that people here are being defensive about Julia. Quite the opposite. If anything, your suggestion tha…

[image] Pierre_Augier: I’d like to be convinced and I’ll study to better understand. Here are a few pointers to get started: Why Python is Slow: Looking Under the Hood | Pythonic Perambulations https://www.youtube.com/watch?v=qCGofLIzX6g Reddit - Dive into anything That last one is by the …

[image] Pierre_Augier: I’m sorry, but it is what is useful in my work for the numerical kernels. If the restricted kind of code that Numba/Pythran can optimize is fine for your application, then lucky for you! We use Julia because we want more flexibility and composability than that, not be…

I’ll post some once I have time to make a POC package.

Hey @Pierre_Augier nice to see you here. This answer is more of a Pythran answer than a Julia answer. Performances On the performance side, I am not sure that there will be a benefit to using julia’s array library over the current Pythran numpy backend, mostly because you guys have done an incredi…

[image] SylvainCorlay: I think that this relative slowness of Julia on these array operations is going to be resolved eventually, but with the current state of things, Pythran is better off with pythonic++ and xsimd as it would be with a Julia backend. Well, the exact issue is known, and for …

[image] SylvainCorlay: To my knowledge, it is not 100% supported by julia to produce compiled binaries that don’t depend on Julia’s runtime I think this is still correct. But I’d like to add that the most recent version of PyJulia added the custom Julia system image support: Custom Julia sys…

[image] ChrisRackauckas: The problem is really that LLVM is getting tripped up with some of what it can and what it can’t assume, since the Julia versions are quite a bit more generic than the ones that Wolf implemented. Well in fact, all non-julia submission were reaching the 12x factor, inc…

How hard would it be to implement Numpy.jl, i.e. Numpy in Julia?

Specific Domains Numerics

Topic		Replies	Views
Julia motivation: why weren't Numpy, Scipy, Numba, good enough? Community history	123	84297	September 21, 2018
Tips for moving from Julia to Numpy/Python? General Usage	33	3363	July 17, 2021
Comparing Python, Julia, and C++ Performance broadcast , python	21	34320	November 1, 2018
Julia's applicable context is getting narrower over time? Offtopic	86	9822	February 23, 2021
Numpy 10x faster than Julia ?! What am I doing wrong ?! [solved - julia faster now] Performance question	37	11318	October 15, 2019

How hard would it be to implement Numpy.jl, i.e. Numpy in Julia?

Related topics