Authors:
Pedro Miguel Moreira
1
;
Luís Paulo Reis
2
and
A. Augusto de Sousa
3
Affiliations:
1
ESTG-IPVC, Instituto Politécnico de Viana do Castelo; DEI/FEUP, Faculdade de Engenharia da Universidade do Porto, Portugal
;
2
DEI/FEUP, Faculdade de Engenharia da Universidade do Porto; LIACC, Laboratório de Inteligência Artificial e Ciência de Computadores, Portugal
;
3
DEI/FEUP, Faculdade de Engenharia da Universidade do Porto; INESC-Porto, Instituto de Engenharia de Sistemas e Computadores do Porto, Portugal
Keyword(s):
Stream Compaction, Parallel Algorithms, Parallel Processing, Graphics Hardware.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Graphics Architectures
;
Parallel Rendering
;
Rendering
;
Rendering Hardware
Abstract:
With the advent of GPU programmability, many applications have transferred computational intensive tasks into it. Some of them compute intermediate data comprised by a mixture of relevant and irrelevant elements in respect to further processing tasks. Hence, the ability to discard irrelevant data and preserve the relevant portion is a desired feature, with benefits on further computational effort, memory and communication bandwidth. Parallel stream compaction is an operation that, given a discriminator, is able to output the valid elements discarding
the rest. In this paper we contribute two original algorithms for parallel stream compaction on the GPU. We tested and compared our proposals with state-of-art algorithms against different data-sets. Results demonstrate that our proposals can outperform prior algorithms. Result analysis also demonstrate that there is not a best algorithm for all data distributions and that such optimal setting is difficult to be achieved without prior k
nowledge of the data characteristics.
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