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Dependency graph approach to load balancing distributed volume visualization

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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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  1. Department of Computer Science, Stony Brook University, Stony Brook, NY, 11794-4400, USA

    Susan Frank & Arie Kaufman

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  1. Susan Frank
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  2. Arie Kaufman
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Correspondence to Susan Frank.

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Frank, S., Kaufman, A. Dependency graph approach to load balancing distributed volume visualization. Vis Comput 25, 325–337 (2009). https://doi.org/10.1007/s00371-008-0295-z

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