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... > The first thing I would check is whether your system becomes starved for memory at this point and virtual memory swapping kicks in.
A common technique for faster plotting of image data is to downsample it before passing it to matplotlib. Same with line plots -- they can be decimated. There is newer/faster path simplification code in SVN trunk that may help with complex line plots (when the path.simplify rcParam is True). I would suggest starting with that as a baseline to see how much performance it already gives over the released version. > 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?... > I've spent a lot of time optimizing the Agg backend (which is already one of the fastest software-only approaches out there), and I'm out of obvious ideas. But a fresh set of eyes may find new things. An advantage of Agg that shouldn't be overlooked is that is works identically everywhere. > 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? > Probably not. If the work of rendering were to be divided among cores, that would probably be done at the C++ level anyway to see any gains. As it is, the problem with plotting many points generally tends to be limited by memory bandwidth anyway, not processor speed. > or even go as far as using something like pyGPU for fast vectorized > computations...? > Perhaps. But again, the computation isn't the bottleneck -- it's usually a memory bandwidth starvation issue in my experience. Using a GPU may only make matters worse. Note that I consider that approach distinct from just using OpenGL to colormap and render the image as a texture. That approach may bear some fruit -- but only for image plots. Vector graphics acceleration with GPUs is still difficult to do in high quality across platforms and chipsets and beat software for speed. > 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 > Hope this helps, Mike -- Michael Droettboom Science Software Branch Operations and Engineering Division Space Telescope Science Institute Operated by AURA for NASA ------------------------------------------------------------------------------ Crystal Reports - New Free Runtime and 30 Day Trial Check out the new simplified licensing option that enables unlimited royalty-free distribution of the report engine for externally facing server and web deployment. http://p.sf.net/sfu/businessobjects _______________________________________________ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
