Massively parallel software rendering for visualizing large-scale data sets

Kwan-Liu Ma; Steven Parker

doi:10.1109/38.933526

Massively parallel software rendering for visualizing large-scale data sets

Kwan-Liu Ma, Steven Parker

Computer Science

Research output: Contribution to journal › Article › peer-review

22 Scopus citations

Abstract

Two different algorithms for rendering large-scale data sets on a parallel machine are described. The first algorithm is designed for distributed-memory parallel architectures to render unstructured-grid volume data. The second one is designed for shared-memory parallel architectures to directly render isosurfaces. For large data sets and smaller images, the ray-tracing method can outperform high-end graphics hardware.

Original language	English (US)
Pages (from-to)	72-83
Number of pages	12
Journal	IEEE Computer Graphics and Applications
Volume	21
Issue number	4
DOIs	https://doi.org/10.1109/38.933526
State	Published - Jul 1 2001

ASJC Scopus subject areas

Software
Computer Graphics and Computer-Aided Design

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10.1109/38.933526

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title = "Massively parallel software rendering for visualizing large-scale data sets",

abstract = "Two different algorithms for rendering large-scale data sets on a parallel machine are described. The first algorithm is designed for distributed-memory parallel architectures to render unstructured-grid volume data. The second one is designed for shared-memory parallel architectures to directly render isosurfaces. For large data sets and smaller images, the ray-tracing method can outperform high-end graphics hardware.",

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AB - Two different algorithms for rendering large-scale data sets on a parallel machine are described. The first algorithm is designed for distributed-memory parallel architectures to render unstructured-grid volume data. The second one is designed for shared-memory parallel architectures to directly render isosurfaces. For large data sets and smaller images, the ray-tracing method can outperform high-end graphics hardware.

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Massively parallel software rendering for visualizing large-scale data sets

Abstract

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