Exploiting Deep Parallel Memory Hierarchies for Ray
Casting Volume Rendering
Proceedings of the 1997 Parallel Rendering Symposium, ACM
SIGGRAPH, Oct. 1997, pp. 15-22,115-116.
- Michael E. Palmer
- Brian Totty
- Stephen Taylor
Previous work in single-processor ray casting methods for volume
rendering has concentrated on algorithmic optimizations to reduce
computational work. Previous work in parallel volume rendering has
concentrated on partitioning, with the goals of maximizing load
balance and minimizing communication between distributed nodes.
Building on our previous work at lower levels of the hierarchy, we
present techniques to efficiently exploit all levels of the deep
memory hierarchy of a distributed Power Challenge Array, on which we
implement a logical global address space for volume blocks with
This focus on the optimal exploitation of the entire memory hierarchy,
from the processor cache, to the interconnection network between
distributed nodes allow us to efficiently render a 7.1 GB dataset.
Our results have implications for the parallel solution of other
problems which, like ray casting, require a global gather operation,
and contain coherence. We discuss implications for the design of a
parallel architecture suited to solving this class of problems.
volume rendering, memory hierarchies, distributed architectures.
Unfortunately, in the symposium proceedings, this paper was printed
with one of the figures missing from the first color plate. The second
color plate had a typo ("frame 49" should be "frame 40"). You can
download the corrected color plates here, or included in the versions
of the paper below.
All files are in gzipped postscript format.
You can download the paper as one file:
Or split into color and black-and-white pages for separate printing:
Slides from Symposium PresentationThe PowerPoint slides from my symposium presentation are also
You can also view the slides as HTML
web pages, although this doesn't provide adequate resolution for
some of the figures.
Machine resources for this work were provided by Silicon Graphics
Corporation, the National Center for Supercomputing Applications
(NCSA), and Peter Schröder at Caltech. This research is sponsored
by the Defense Advanced Research Projects Agency (DARPA) under
contract number DABT63-95-C-0116, and AASERT award number
N0014-93-1-0843. The Visible Male and Visible Female datasets were
courtesy the National Library of Medicine. The Vorticity dataset was
courtesy the Laboratory for Computational Science and Engineering at
the University of Minnesota.