| Texto completo | |
| Autor(es): |
Santana, Fabiana
;
Bravo Pariente, Cesar Alberto
;
Saraiva, Antonio Mauro
;
Zhang, C
;
Palanisamy, B
;
Khan, L
;
Sarvestani, SS
Número total de Autores: 7
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2017 IEEE 18TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IEEE IRI 2017); v. N/A, p. 9-pg., 2017-01-01. |
| Resumo | |
Species distribution modeling (SDM) calculates a species' probabilistic distribution by combining Environmental raster layers with species datasets. Such models can help to answer complex questions in Ecology/Biology/Health, e.g., by calculating impacts of climate changes in Biodiversity, or the potential for a disease spread (vectors' modeling). Machine learning is largely applied in SDM, being the Genetic Algorithm for Rule-set Production (GARP) one of the most reliable solutions. However, GARP's convergence needs to speedup under certain conditions (high resolution or number of layers), for which this paper proposes P-GARP, a parallel, scalable implementation of GARP. P-GARP was implemented onto a SGI Altix XE 1300 cluster with 2 quad-core processors/node. Preliminary results show an expressive 3.2/node speedup. Premature convergence is not observed in PGARP and its accuracy is very similar to GARP ' s. Effective solutions to improve this speedup in even larger scale are proposed, along with a discussion about P-GARP correctness and efficiency. (AU) | |
| Processo FAPESP: | 04/11012-0 - Um ambiente computacional para a modelagem da distribuição de espécies |
| Beneficiário: | Vanderlei Perez Canhos |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |