Efficient parallel implementations for surface subdivision

Authors:
E. J. Padrón;M. Amor;M. Bóo;R. Doallo
Affiliations:
University of A Coruña, E-15071 A Coruña, Spain;University of A Coruña, E-15071 A Coruña, Spain;University of Santiago de Compostela, E-15782 Santiago de Compostela, Spain;University of A Coruña, E-15071 A Coruña, Spain
Venue:
EGPGV '02 Proceedings of the Fourth Eurographics Workshop on Parallel Graphics and Visualization
Year:
2002

Citing 6
Cited 2

A butterfly subdivision scheme for surface interpolation with tension control

ACM Transactions on Graphics (TOG)
Interpolating Subdivision for meshes with arbitrary topology

SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Towards hardware implementation of loop subdivision

HWWS '00 Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on Graphics hardware
Hardware support for adaptive subdivision surface rendering

Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on Graphics hardware
Improved Triangular Subdivision Schemes

CGI '98 Proceedings of the Computer Graphics International 1998
Stationary subdivision and multiresolution surface representations

Stationary subdivision and multiresolution surface representations

A realtime GPU subdivision kernel

ACM SIGGRAPH 2005 Papers
Parallel subdivision surface rendering and animation on the cell BE processor

Proceedings of the Conference on Design, Automation and Test in Europe

Quantified Score

Hi-index	0.00

Visualization

Abstract

Achieving an efficient surface subdivision is an important issue today in computer graphics, geometric modeling, and scientific visualization. In this paper we present two parallel versions of the Modified Butterfly algorithm. Both versions are based on a coarse-grain approach, that is, the original mesh is subdivided into small groups and each processor performs the triangles subdivision for a set of groups of the mesh. First approach sorts the groups in decreasing order of number of triangles per group, and then the sorted groups are cyclically distributed on the processors in order to achieve a good load distribution. In the second parallel version the processors can dynamically balance the work load by passing groups from heavier loaded processors to lighter ones, achieving in that way a better load balance. Finally, we evaluate the algorithms on two different systems: a SGI Origin 2000 and a Sun cluster. Good performances in terms of speedup have been obtained using both static and dynamic parallel implementations.