Parallel generation of architecture on the GPU

Steinberger, Markus; Kenzel, Michael; Kainz, Bernhard K.; Müller, Jörg; Wonka, Peter; Schmalstieg, Dieter

Parallel generation of architecture on the GPU

Type

Article

Authors

Steinberger, Markus
Kenzel, Michael
Kainz, Bernhard K.
Müller, Jörg
Wonka, Peter

Schmalstieg, Dieter

KAUST Department

Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Visual Computing Center (VCC)

Online Publication Date

2014-06-01

Print Publication Date

2014-05

Date

2014-06-01

Abstract

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. © 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.

Citation

Steinberger, M., Kenzel, M., Kainz, B., Müller, J., Peter, W., & Schmalstieg, D. (2014). Parallel generation of architecture on the GPU. Computer Graphics Forum, 33(2), 73–82. doi:10.1111/cgf.12312

Acknowledgements

This research was funded by the Austrian Science Fund (FWF): P23329.