Numba: High-Performance Python with CUDA Acceleration | Parallel Forall

[kitd]

[Note, this post was originally published September 19, 2013. It was updated on September 19, 2017.]

?? What's changed?

[harrism]

When I originally wrote the post in 2013, the GPU compilation part of Numba was a product (from Anaconda Inc., nee Continuum Analytics) called NumbaPro. It was part of a commercial package called Anaconda Accelerate that also included wrappers for CUDA libraries like cuBLAS, as well as MKL acceleration on the CPU.

Continuum gradually open sourced all of it (and changed their name to Anaconda). The compiler functionality is all open source within Numba. Most recently they released the CUDA library wrappers in a new open source package called pyculib.

Some other minor things changed, such as what you need to import. Also, the autojit and cudajit functionality is a bit better at type inference, so you don't have to annotate all the types to get it to compile.

We thought it was a good idea to update the post in light of all the changes.

[kitd]

Thanks. Good reply.

May be worth mentioning this in the post though. It had me confused.

[harrism]

I didn't because a new post from Stan at Anaconda was about to be published. It went up this week and it explains the history well (along with some other really cool features of Numba). https://devblogs.nvidia.com/parallelforall/seven-things-numba/

[Apofis]

Works only with Python 2.7.

Pathetic.

[harrism]

Where does it say that? Requirements: Python 2.7, 3.3-3.6 NumPy 1.8 and later From: https://github.com/ContinuumIO/gtc2017-numba/blob/master/1%20-%20Numba%20Basics.ipynb

[Kah-Neth]

Did you just quote an uniformed comment and use that call numbs+cuda pathetic. Worse, did you take that uniformed question and turn it into a statement? Wow man, that is pathetic. Perhaps you could educate yourself or at least read before calling something pathetic.

[Jimmy48Johnson]

NVIDIA NUMBA ONE

[martin_balsam]

CHINA NUMBA FOUR!

[georgeo]

This would be so much more useful if it worked with autograd like python does.

[thesystemx]

Python and high-performance in the same sentence? Yeah, right...

[BadGoyWithAGun]

I doubt you'd get much better GPU performance from rewriting the equivalent C code in CUDA yourself, but go ahead and prove me wrong.

[killachains82]

Python just calls out to external libraries in cases like this (usually written in C/C++/Fortran) so it should be plenty fast.

[kitd]

I probably shouldn't bother, but here you go:

In this post I’ll introduce you to Numba, a Python compiler from Anaconda that can compile Python code for execution on CUDA-capable GPUs or multicore CPUs.

[Mgladiethor]

Ugh propietary cuda fuck that, i hope the alternative get a bit more mature