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First, a short note on the structure of our collision detection
program. An overview of the processing pipeline is given in Figure
1. We assume that before entering the pipeline a
scene has been loaded containing some objects. In our implementation,
these are LightWave objects listed in a little script. We also use
this script to set several switches that determine the operating mode.
The object-object weakness strategy is the Sweep
and Prune (S&P) algorithm as presented previously. For
the face level intersection tests we have the following algorithms:
Axis Aligned Bounding Box trees (AABB), Oriented Bounding Box
trees with or without computation of the convex hull in the box
orientation calculation (OBB and OBBCV) and lastly the V-Clip
algorithm ([Mir98]) which I didn't mention before and
won't say anything about since it wasn't parallelized.
As you can see from Figure 1, you can skip the S&P
steps, since in Slave Mode this step will be done by the Master. It's also
possible that in a particular application the scenes are so dense that
the S&P check can't deliver a speed increase and is best switched
off. Also, the face-level detection can be passed on to slaves, which is
of particular interest in this paper.
Figure 1:
The collision detection pipeline
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For our implementation, we used the Parallel
Virtual Machine library, a library that allows a network of computers
to be used as a single parallel machine.
Later on, we will discuss what exactly the Parallel Virtual
Machine (PVM) library offers. But first we'll have to temper
expectations a bit.
Next: Ahmdal's Law
Up: Introduction to Advanced Computer
Previous: Hierarchical Collision Detection Methods
Marc Ramaekers
5/17/1999