Re: [matplotlib-devel] Large datasets performance....

Hello,

To give you some hints on performances using OpenGL, you can have a look
at glumpy: http://www.loria.fr/~rougier/tmp/glumpy.tgz
(It requires pyglet for the OpenGL backend).

It is not yet finished but it is usable. Current version allows to
visualize static numpy float32 array up to 8000x8000  and dynamic numpy
float32 array around 500x500 depending on GPU hardware (dynamic means
that you update image at around 30 fps/second).

The idea behind glumpy is to directly translate a numpy array into a
texture and to use shaders to make the colormap transformation and
filtering (nearest, bilinear or bicubic).

Nicolas



On Wed, 2009-06-17 at 07:02 -0700, vehemental wrote:
> Hello,
> 
> I'm using matplotlib for various tasks beautifully...but on some occasions,
> I have to visualize large datasets (in the range of 10M data points) (using
> imshow or regular plots)...system start to choke a bit at that point...
> 
> I would like to be consistent somehow and not use different tools for
> basically similar tasks...
> so I'd like some pointers regarding rendering performance...as I would be
> interested to be involved in dev is there is something to be done....
> 
> To active developers, what's the general feel does matplotlib have room to
> spare in its rendering performance?...
> or is it pretty tied down to the speed of Agg right now?
> Is there something to gain from using the multiprocessing module now
> included by default in 2.6?
> or even go as far as using something like pyGPU for fast vectorized
> computations...?
> 
> I've seen around previous discussions about OpenGL being a backend in some
> future...
> would it really stand up compared to the current backends? is there clues
> about that right now?
> 
> thanks for any inputs! :D
> bye