Distributed terascale volume visualization using distributed shared virtual memory

Each cluster node is running its own pipeline for ray-casting, virtual vol- ume and memory management, and paging. Ray-casting is per- formed on a huge virtual 3D volume, which is conceptually given on a regular grid in 3D. However, this volume is virtual because the data that are sampled by the ray...

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Bibliographic Details
Published in:2011 IEEE Symposium on Large Data Analysis and Visualization pp. 127 - 128
Main Authors: Beyer, J., Hadwiger, M., Schneider, J., Won-Ki Jeong, Pfister, H.
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2011
Subjects:
Data visualization
Graphics processing unit
Octrees
Rendering (computer graphics)
Switches
Three dimensional displays
ISBN:9781467301565, 1467301566
Online Access:Get full text
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Summary:Each cluster node is running its own pipeline for ray-casting, virtual vol- ume and memory management, and paging. Ray-casting is per- formed on a huge virtual 3D volume, which is conceptually given on a regular grid in 3D. However, this volume is virtual because the data that are sampled by the ray-caster are only created on demand (i.e., read from disk or reconstructed on-the-fly), as determined by the actual visibility of small cubical blocks or memory pages of 32 voxels each. In order to be able to adapt the data resolution used for ray-casting to the output screen resolution, we represent the whole virtual volume as a virtual octree multi-resolution hier- archy, which is mapped to a large shared virtual memory address space.
ISBN:9781467301565
1467301566
DOI:10.1109/LDAV.2011.6092332