In part 1, I did some testing with a pretty simple scene, where you were looking at multiple frames rendered within a minute – a frame rendered every 10-20 seconds give or take. The diminishing returns when dishing out a simple scene to be rendered across the network are HUGE. In fact, I did another test with an AMD 6-core machine as the master, and found that repeating the tests from part 1 resulted in a DECREASE in frames rendered per minute regardless of the combination of machines used. Solo was always better with the 6-core, at a steady rate of 7 frames per minute (or one every 8-9 seconds). Adding any slaves resulted in a reduction of speed to between 5-6 frames per minute. 8 combinations were tried in addition to the solo method, and NONE yielded an improvement.
This time I’m looking at a more complex scene (or at least… a more complex render of it). I took the same scene, chose a section of frames to render that tended to be slowest, and cranked up a few settings in the mental ray options. I bumped up the resolution and added motion blur. Now instead of seconds, I was talking 2-3 minutes to render each frame.
The goal was to reduce the effect of network overhead on the results, with more time spent rendering, and a much smaller percent being taken up by Mental Ray Satellite’s network distribution.
The results were… interesting.
The same 36 frames were rendered for each run, and I used the results from 35 of them (using the 1st as a time stamp).
Note: ignore the last 4 columns - it's just data that I'd added to the chart to look for correlation/scaling. Focus on the 4th data column ("improvement as % of solo render") to see the benefit/decrease of additional machines. I apologize for not simply highlighting that column in the image.
A few notes as they relate to continue reading…
