Ray tracing large distributed datasets using ray caches
Date
2012-03-09
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Abstract
Many large scale simulations now produce datasets that are signi cantly
larger than can typically be stored in memory on a visualization system. Visualization
algorithms then become ine ective and stall since the data must be
paged to disk. Recently, in-situ visualization has received renewed attention
for visualizing large datasets that are distributed among many processors during
a simulation. It takes advantage of the fact that the full dataset is already
in main memory, distributed among multiple processors. Visualization in this
environment then requires communication which can be more expensive than
disk access. The goal of this thesis was to develop an in-situ visualization
technique using ray tracing that employs ray caches to reduce communication
overhead. Ray caches attempt to replace a communication operation with a
less expensive cache search operation. A prototype implemented on Sharcnet
shows ray caching can signi cantly improve overall performance at a small cost
to image quality.
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Keywords
Visualization, Cluster, Sharcnet, Simulation, In-situ