Parallel Marching Blocks: A Practical Isosurfacing Algorithm for Large Data on Many-Core Architectures

Interactive isosurface visualisation has been made possible by mapping algorithms to GPU architectures. However, current state‐of‐the‐art isosurfacing algorithms usually consume large amounts of GPU memory owing to the additional acceleration structures they require. As a result, the continued limit...

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Vydané v:Computer graphics forum Ročník 35; číslo 3; s. 211 - 220
Hlavní autori: Liu, Baoquan, Clapworthy, Gordon J., Dong, Feng, Wu, Enhua
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.06.2016
Predmet:
Algorithms
Analysis
Architecture
Categories and Subject Descriptors (according to ACM CCS)
Central processing units
Computer graphics
Computer science
Consumption
CPUs
Footprints
I.3.3 [Computer Graphics]: Picture/Image Generation-Viewing Algorithms
Interactive
Interactive computer systems
Optimization
Platforms
State of the art
Studies
Visualization
ISSN:0167-7055, 1467-8659
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Shrnutí:Interactive isosurface visualisation has been made possible by mapping algorithms to GPU architectures. However, current state‐of‐the‐art isosurfacing algorithms usually consume large amounts of GPU memory owing to the additional acceleration structures they require. As a result, the continued limitations on available GPU memory mean that they are unable to deal with the larger datasets that are now increasingly becoming prevalent. This paper proposes a new parallel isosurface‐extraction algorithm that exploits the blocked organisation of the parallel threads found in modern many‐core platforms to achieve fast isosurface extraction and reduce the associated memory requirements. This is achieved by optimising thread co‐operation within thread‐blocks and reducing redundant computation; ultimately, an indexed triangular mesh can be produced. Experiments have shown that the proposed algorithm is much faster (up to 10×) than state‐of‐the‐art GPU algorithms and has a much smaller memory footprint, enabling it to handle much larger datasets (up to 64×) on the same GPU.
Bibliografia:ark:/67375/WNG-GDKJ8V0J-H
Supporting InformationSupporting InformationSupporting Information
ArticleID:CGF12897
istex:6759531E780C947A0A085318670FBFADC67D5F02
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12897