Explorando Mapeamentos de Threads e Dados para Melhoria de Desempenho de Algoritmos de Aprendizado de Máquina
Resumo
Algoritmos de Aprendizado de Máquina (ML) são cada vez mais utilizados em diversos problemas científicos e industriais, tendo como uma preocupação importante o tempo de execução destes algoritmos. Neste trabalho exploramos mapeamentos de threads e dados e o impacto em novos algoritmos de ML. Resultados experimentos mostraram que os algoritmos SRCNN e MobileNet apresentaram tempos de execução até 15.1% menores e mais uniformes.
Palavras-chave:
Avaliação, Medição e Predição de Desempenho, Aplicações em Agricultura, Biologia, Engenharia, Física, Matemática, Medicina, Mercado Financeiro, Nanociências, Óleo e Gás, Química e outras áreas
Referências
Culkin, R. and Das, S. R. (2017). Machine learning in finance: the case of deep learning for option pricing. Journal of Investment Management, 15(4):92–100.
Diener, M., Cruz, E. H., Alves, M. A., Navaux, P. O., and Koren, I. (2016). Affinitybased thread and data mapping in shared memory systems. ACM Computing Surveys (CSUR), 49(4):1–38.
Dong, C., Loy, C. C., He, K., and Tang, X. (2015). Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 38(2):295–307.
Ignatov, A., Timofte, R., Kulik, A., Yang, S., Wang, K., Baum, F., Wu, M., Xu, L., and Van Gool, L. (2019). Ai benchmark: All about deep learning on smartphones in 2019. arXiv preprint arXiv:1910.06663.
Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2):19–50.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510–4520.
Serpa, M. S., Cruz, E. H., Diener, M., Krause, A. M., Navaux, P. O., Panetta, J., Farrés, A., Rosas, C., and Hanzich, M. (2019). Optimization strategies for geophysics models on manycore systems. The International Journal of High Performance Computing Applications, 33(3):473–486.
Serpa, M. S., Krause, A. M., Cruz, E. H., Navaux, P. O. A., Pasin, M., and Felber, P. (2018). Optimizing machine learning algorithms on multi-core and many-core architectures using thread and data mapping. In 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pages 329–333. IEEE.
Stavens, D. M. et al. (2011). Learning to drive: Perception for autonomous cars. PhD thesis, Citeseer.
Diener, M., Cruz, E. H., Alves, M. A., Navaux, P. O., and Koren, I. (2016). Affinitybased thread and data mapping in shared memory systems. ACM Computing Surveys (CSUR), 49(4):1–38.
Dong, C., Loy, C. C., He, K., and Tang, X. (2015). Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 38(2):295–307.
Ignatov, A., Timofte, R., Kulik, A., Yang, S., Wang, K., Baum, F., Wu, M., Xu, L., and Van Gool, L. (2019). Ai benchmark: All about deep learning on smartphones in 2019. arXiv preprint arXiv:1910.06663.
Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2):19–50.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510–4520.
Serpa, M. S., Cruz, E. H., Diener, M., Krause, A. M., Navaux, P. O., Panetta, J., Farrés, A., Rosas, C., and Hanzich, M. (2019). Optimization strategies for geophysics models on manycore systems. The International Journal of High Performance Computing Applications, 33(3):473–486.
Serpa, M. S., Krause, A. M., Cruz, E. H., Navaux, P. O. A., Pasin, M., and Felber, P. (2018). Optimizing machine learning algorithms on multi-core and many-core architectures using thread and data mapping. In 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pages 329–333. IEEE.
Stavens, D. M. et al. (2011). Learning to drive: Perception for autonomous cars. PhD thesis, Citeseer.
Publicado
15/04/2020
Como Citar
CAMARGO, Matheus; SERPA, Matheus; CARASTAN-SANTOS, Danilo; CARISSIMI, Alexandre; NAVAUX, Philippe Olivier Alexandre.
Explorando Mapeamentos de Threads e Dados para Melhoria de Desempenho de Algoritmos de Aprendizado de Máquina. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DA REGIÃO SUL (ERAD-RS), 20. , 2020, Santa Maria.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 129-132.
ISSN 2595-4164.
DOI: https://doi.org/10.5753/eradrs.2020.10773.
