Dependency graph approach to load balancing distributed volume visualization |
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Authors: | Susan Frank Arie Kaufman |
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Affiliation: | (1) Department of Computer Science, Stony Brook University, Stony Brook, NY 11794-4400, USA |
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Abstract: | We present a framework that uses data dependency information to automate load balanced volume distribution and ray-task scheduling
for parallel visualization of massive volumes. This dependency graph approach improves load balancing for both ray casting
and ray tracing. The main bottlenecks in distributed volume rendering involve moving data across the network and loading memory
into rendering hardware. Our load balancing solution combines static network distribution with dynamic ray-task scheduling.
At the core of the dependency graph approach are the flex-block tree, introduced in this paper, and the cell-tree. The flex-block
tree is similar to a kd-tree except that leaf nodes are cells containing a combination of empty space and tightly cropped
subvolumes, or flex-blocks. A main contribution of this paper is the moving walls algorithm, which uses dynamic programming to create a flex-block partition. We show results for optimizing distributed ray
cast rendering using a time cost function. We compare data distribution using the moving walls algorithm, with distribution
using a recursive solution, and with a grid combined with a local kd-tree partition on each render-node.
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Keywords: | Load balancing Task scheduling Large-scale volume data Visualization cluster |
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