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| Title | Multi-GPU Volume Rendering using MapReduce
(Article) |
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| in | 1st International Workshop on MapReduce and its Applications |
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| Author(s) |
Jeff A. Stuart, Cheng-Kai Chen, Kwan-Liu Ma, John D. Owens |
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| Year |
June 2010
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| Location | Chicago, IL |
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| Date | June 22, 2010 |
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| Abstract |
In this paper we present a multi-GPU parallel volume rendering implemention
built using the MapReduce programming model. We give implementation details of
the library, including specific optimizations made for our rendering and
compositing design. We analyze the theoretical peak performance and bottlenecks
for all tasks required and show that our system significantly reduces
computation as a bottleneck in the ray-casting phase. We demonstrate that our
rendering speeds are adequate for interactive visualization (our system is
capable of rendering a $1024^3$ floating-point sampled volume in under one
second using 8 GPUs), and that our system is capable of delivering both in-core
and out-of-core visualizations. We argue that a multi-GPU MapReduce library is
a good fit for parallel volume renderering because it is easy to program for,
scales well, and eliminates the need to focus on I/O algorithms thus allowing
the focus to be on visualization algorithms instead. We show that our system
scales with respect to the size of the volume, and (given enough work) the
number of GPUs.
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