Hierarchical Z-buffer visibility
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
A Sorting Classification of Parallel Rendering
IEEE Computer Graphics and Applications
Visibility culling using hierarchical occlusion maps
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Distributed rendering for scalable displays
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
WireGL: a scalable graphics system for clusters
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Chromium: a stream-processing framework for interactive rendering on clusters
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
GigaWalk: interactive walkthrough of complex environments
EGRW '02 Proceedings of the 13th Eurographics workshop on Rendering
Delay streams for graphics hardware
ACM SIGGRAPH 2003 Papers
GPU Cluster for High Performance Computing
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Functionality Distribution for Parallel Rendering
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Parallel culling and sorting based on adaptive static balancing
Computers in Entertainment (CIE) - SPECIAL ISSUE: Games
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Occlusion culling, level-of-detail and parallel rendering are key techniques to accelerate interactively rendering of large dataset. Although each of these techniques has been independently researched to an extensive degree and some systems have been developed to combine them together. Unfortunately, parallel occlusion culling, i. e. distributing the computation of occlusion culling onto multiple computing nodes such as a CPUs cluster or GPUs cluster, has rarely been touched partly because most of existing occlusion culling algorithms do not scale well when parallelized.To address this issue, we first introduce a novel occlusion culling algorithm which uses occlusion query function provided by current GPUs and adopts a visibility predictor based on temporal coherence to reduce the count of occlusion queries. Furthermore, different schemes to parallelize this occlusion culling algorithm on a GPUs cluster are proposed including data parallelism schemes and functionality parallelism schemes. Data parallelism scheme decomposes data for occlusion culling into separate parts and maps the computation of them onto different cluster nodes for parallel processing. Functionality parallelism scheme assembles multiple cluster nodes as an occlusion culling pipeline which outputs steady image stream. We show a number of viable solutions to the special problems on parallelizing this occlusion culling algorithm, such as the transferring of data dependency and the load-balancing of occlusion culling pipeline. We report on the experimental results to demonstrate the efficiency of the proposed parallelism schemes based on the novel occlusion culling algorithm.