[Matplotlib-devel] [matplotlib-devel] Make matplotlib running on GPU/CUDA

Chris Barker chris.barker at noaa.gov
Thu Sep 21 17:13:17 EDT 2017

On Wed, Sep 13, 2017 at 12:31 AM, Francesco Faccenda <f.faccenda86 at gmail.com
> wrote:

> I have to admit I already stumbled on VisPy while doing my research on the
> web. Still, I've got a lot of code already working with *matplotlib*.
> Indeed, not only I plot data with it, but i manage a lot of *mpl events*
> to provide the users usefool tools, like lines picking, tooltips, lines
> copy/paste, square selectors for multiple selections, context menu and so
> on. Moreover, I got matplotlib *embedded *on *wxpython *as well. While at
> the beginning few lines were managed and noone complained, now that big
> amout of data has to be displayed, the non-GPU core of the library is
> starting to show its limits.
> Since matplotlib is a reference library for this kind of  applications, I
> thought it deserved an update in this direction.

Well, As I understand it, VisPY made some effort to be compatible with the
MPL API -- but that is going to depend on how much you use the lower-level
parts f the API -- things like the transform, etc. to take advantage of GPU
rendering, all the transforms, etc needs to be pushed to the GPU, so the
architecture (and API) needs to be quite different.

> If anyone is willing to do so, I'm available to discuss possible solutions
> and also provide any help I can give.

As Ben pointed out, and I was trying to make clear -- it really isn't a
matter of "just" writing an OpenGL backend -- there really needs to be a
major restructuring.

And VisPy is pretty much that project. Whether it is feature complete,
robust and maintained enough for your use-cases, I have no idea, but even
if not -- you'll probably be better off contributing to that effort than
starting all over with trying to make an GPU_based OPenGL back-end.

However -- maybe there is another option:

Taking full advantage of GPUs does require a full restructuring, but maybe
there are other ways to get better performance -- for instance, optimizing
the transform code, etc:

Using the GPU with PyCuda or [what the heck is the name of the more general
GPU-using lib??]

using numba

Maybe there is still room for Cython, etc....

In short, profiling MPL carefully, to see where the performance bottlenecks

With modern hardware, actually rendering stuff is no longer the slow part
of visualization. Rather, it's pushing data to the renderer, transforming
data etc.

This is why to take advantage of the GPU, you need to do the
transformations, etc on the GPU -- which the MPL architecture doesn't make
easy by dropping in a new back-end.

Which is why VisPy built a new architecture from the bottom up.



Christopher Barker, Ph.D.

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Chris.Barker at noaa.gov
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