On 29/09/2014 17:31, cloudyroo wrote:
> I was wondering if anyone (up to and including developers) knew any way to work
> out how much CPU work/time would be needed to render an image based on its size
> and render options. It seems like it must be possible to find out.
> Thanks,
> Ross
> 
> 


For the same options on the same scene, the render time should scale
linearly with each dimensions (as long as the view remains the same)....
well, unless you hit a dark spot that was missed by the small scale
picture, or you use something like radiosity and/or photons.

Of course, that's not taking into account the parse time which is fixed
for the same scene.

So you already have an equation like:
 cpu = parse + height*weight*mystery.

But mystery can be influenced by the content, placement and settings, if
not other things. And it might hide a bit of additional complexity too
(such as it's own constant for photons, and an exploding factor for
anti-aliasing as details get bigger).

Short of that, the best answer so far is the one from Doctor John.

Of course, if you have a specific scene, with some specific settings (or
a set of them), you could use a profiling tools (such as
valgrind/callgrind) to get the cost of the rendering at the various
resolutions.

But such data would be useless to predicts a different scene with accuracy.

Just collecting the valgrind/callgrind data will slow down the rendering
by about x50 to x200, and getting enough points to get a predictive
model is going to be... a test to your patience. And that's just
generating the data, you still have to collect them and find their
relations.

If you are lucky to have an Intel processor, Intel has a faster
profiling tools (which requires a kernel driver to be installed, if you
are on linux) in its wonderful (but expensive) compiler/debugger suits.

Yet, it won't reduce the number of renders to run.

Povray is not triangle-based, you cannot infer the render time of a
torus from the one of a sphere.

-- 
IQ of crossposters with FU: 100 / (number of groups)
IQ of crossposters without FU: 100 / (1 + number of groups)
IQ of multiposters: 100 / ( (number of groups) * (number of groups))

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