Hi,
as I explained in some other mail to this list, I am currently working
on a graph based image processing framework using CUDA. Basically, this
is independent from OSG, but I am using OSG for my example application :-)
For my first implemented postprocessing algorithm I need color and depth
data. As I want the depth to be linearized between 0 and 1, I used a
shader for that and also I render it in a separate pass to the color.
This stuff is then fetched from the GPU to the CPU by directly attaching
osg::Images to the cameras. This works perfectly, but is quite a bit
slow, as you might already have suspected, because the data is also
processed in CUDA kernels later, which is quite a back and forth ;-)
In fact, my application with three filter kernels based on CUDA (one
gauss blur with radius 21, one image subtract and one image "pseudo-add"
(about as elaborate as a simple add ;-)) yields about 15 fps with a
resolution of 1024 x 1024 (images for normal and absolute position
information are also rendered transferred from GPU to CPU here).
So with these 15 frames, I thought it should perform FAR better when
avoiding that GPU <-> CPU copying stuff. That's when I came across the
osgPPU-cuda example. As far as I am aware, this uses direct mapping of
PixelBuferObjects to cuda memory space. This should be fast! At least
that's what I thought, but running it at a resolution of 1024 x 1024
with a StatsHandler attached shows that it runs at just ~21 fps, not
getting too much better when the cuda kernel execution is completely
disabled.
Now my question is: Is that a general (known) problem which cannot be
avoided? Does it have anything to do with the memory mapping functions?
How can it be optimized? I know that, while osgPPU uses older CUDA
memory mapping functions, there are new ones as of CUDA 3. Is there a
difference in performance?
Any information on this is appreciated, because it will really help me
to decide wether I should integrate buffer mapping or just keep the
copying stuff going :-)
Best Regards
-Thorsten
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