Parallel generation of architecture on the GPU

In this paper, we present a novel approach for the parallel evaluation of procedural shape grammars on the graphics processing unit (GPU). Unlike previous approaches that are either limited in the kind of shapes they allow, the amount of parallelism they can take advantage of, or both, our method su...

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Vydané v:Computer graphics forum Ročník 33; číslo 2; s. 73 - 82
Hlavní autori: Steinberger, Markus, Kenzel, Michael, Kainz, Bernhard, Müller, Jörg, Peter, Wonka, Schmalstieg, Dieter
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.05.2014
Predmet:
Analysis
Categories and Subject Descriptors (according to ACM CCS)
Central processing units
Computer graphics
CPUs
Data processing
Derivation
Grammars
Graphics processing units
I.3.1 [Computer Graphics] Hardware Architecture-Parallel processing
I.3.5 [Computer Graphics] Computational Geometry and Object Modeling
Scheduling
Shape grammar
Shape memory
State of the art
Studies
ISSN:0167-7055, 1467-8659
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Popis
Shrnutí:In this paper, we present a novel approach for the parallel evaluation of procedural shape grammars on the graphics processing unit (GPU). Unlike previous approaches that are either limited in the kind of shapes they allow, the amount of parallelism they can take advantage of, or both, our method supports state of the art procedural modeling including stochasticity and context‐sensitivity. To increase parallelism, we explicitly express independence in the grammar, reduce inter‐rule dependencies required for context‐sensitive evaluation, and introduce intra‐rule parallelism. Our rule scheduling scheme avoids unnecessary back and forth between CPU and GPU and reduces round trips to slow global memory by dynamically grouping rules in on‐chip shared memory. Our GPU shape grammar implementation is multiple orders of magnitude faster than the standard in CPU‐based rule evaluation, while offering equal expressive power. In comparison to the state of the art in GPU shape grammar derivation, our approach is nearly 50 times faster, while adding support for geometric context‐sensitivity.
Bibliografia:istex:2CCEC08600947BBD7BDC0B6E12B5D98096C5575F
ArticleID:CGF12312
ark:/67375/WNG-X5B8162S-H
Supporting Information
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12312