Em Busca de uma Inteligência Artificial Ecologicamente Viável: Um estudo de caso do Consumo Energético de Algoritmos de íArvore de Decisão
Resumo
O uso da Inteligência Artificial vem apresentando crescimento acelerado dado à sua utilização na solução de problemas em diversos domínios de aplicação. Este sucesso é resultado da convergência entre grande quantidade de dados, computação de alto desempenho e precisão dos algoritmos de Aprendizado de Máquina (AM). Mesmo com a relevância dos algoritmos de AM, pouco se sabe sobre seus requisitos computacionais e consumo energético, o que tornou-se tarefa importante para alcançar uma computação mais ecológica. O objetivo deste trabalho é avaliar o consumo de energia dos algoritmos de íArvore de Decisão, a fim de identificar os hotspots de energia dos mesmos. E ainda, investigar a emissão de CO2 equivalente associada aos algoritmos.Referências
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Siegmund, N., Grebhahn, A., Apel, S., and Kästner, C. (2015). Performance-inuence models for highly congurable systems. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, pages 284–294. ACM.
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Wu, X., Taylor, V., Cook, J., and Mucci, P. J. (2016). Using performance-power modeling to improve energy efciency of hpc applications. Computer, 49(10):20–29.
Yang, T.-J., Chen, Y.-H., and Sze, V. (2017). Designing energy-efcient convolutional neural networks using energy-aware pruning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5687–5695.
Avelar, V., Azevedo, D., French, A., and Power, E. N. (2012). Pue: a comprehensive examination of the metric.
Bernardo, F., Silva, G., Gritz, M., Ferro, M., and Schulze, B. (2020). Avaliação do consumo de energia e o impacto da emissão de co2 para algoritmos de inteligência articial. In Anais do XIV Brazilian e-Science Workshop, pages 81–88, Porto Alegre, RS, Brasil. SBC.
Bifet, A., Holmes, G., Kirkby, R., and Pfahringer, B. (2010). Moa: Massive online analysis http://sourceforge.net/projects/moa-datastream. Journal of Machine Learning Research (JMLR).
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A. (1984). Classication and regression trees. CRC press.
Ferreira, A. R. et al. (2017). Um modelo analítico para estimar o consumo de energia de sistemas multi-camadas no nível de transação. Master's thesis, Universidade Federal de Goiás.
Flach, P. (2012). Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press.
Garcia-Martin, E., Lavesson, N., and Grahn, H. (2017). Identication of energy hotspots: In International Conference on Green,
A case study of the very fast decision tree. Pervasive, and Cloud Computing, pages 267–281. Springer.
García-Martín, E., Rodrigues, C. F., Riley, G., and Grahn, H. (2019). Estimation of energy consumption in machine learning. Parallel and Distributed Computing, 134:75–88.
Klôh, V., Gritz, M., Schulze, B., and Ferro, M. (2019). Towards an autonomous framework for hpc optimization: Using machine learning for energy and performance modeling. In Anais do XX Simpósio em Sistemas Computacionais de Alto Desempenho, pages 438–445, Porto Alegre, RS, Brasil. SBC.
Li, D., Chen, X., Becchi, M., and Zong, Z. (2016). Evaluating the energy efciency of In 2016 IEEE International deep convolutional neural networks on cpus and gpus. Conferences on Big Data and Cloud Computing, Social Computing and Networking, Sustainable Computing and Communications, pages 477–484. IEEE.
Malakar, P., Balaprakash, P., Vishwanath, V., Morozov, V., and Kumaran, K. (2018). Benchmarking machine learning methods for performance modeling of scientic applications. In 2018 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS), pages 33–44.
Miranda, M. M. d. (2012). Fator de emissão de gases de efeito estufa da geração de energia elétrica no Brasil: implicações da aplicação da Avaliação do Ciclo de Vida. PhD thesis, Universidade de São Paulo.
Noureddine, A., Rouvoy, R., and Seinturier, L. (2015). Monitoring energy hotspots in software. Automated Software Engineering, 22(3):291–332.
Olson, R. S., La Cava, W., Orzechowski, P., Urbanowicz, R. J., and Moore, J. H. (2017). Pmlb: a large benchmark suite for machine learning evaluation and comparison. BioData mining, 10(1):1–13.
Quinlan, J. R. (1996). Improved use of continuous attributes in c4.5. Journal of articial intelligence research, 4:77–90.
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.
Siegmund, N., Grebhahn, A., Apel, S., and Kästner, C. (2015). Performance-inuence models for highly congurable systems. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, pages 284–294. ACM.
Strubell, E., Ganesh, A., and McCallum, A. (2019). Energy and policy considerations for deep learning in nlp. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3645–3650.
Villani, C., Bonnet, Y., schoenauer, m., berthet, c., levin, f., cornut, a. c., and Rondepierre, B. (2018). For a meaningful articial intelligence: towards a french and european strategy. Conseil national du numérique.
Wu, X., Taylor, V., Cook, J., and Mucci, P. J. (2016). Using performance-power modeling to improve energy efciency of hpc applications. Computer, 49(10):20–29.
Yang, T.-J., Chen, Y.-H., and Sze, V. (2017). Designing energy-efcient convolutional neural networks using energy-aware pruning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5687–5695.
Publicado
21/10/2020
Como Citar
BERNARDO, Felipe; FERRO, Mariza; VIEIRA, Vitor; SILVA, Gabrieli; SCHULZE, Bruno.
Em Busca de uma Inteligência Artificial Ecologicamente Viável: Um estudo de caso do Consumo Energético de Algoritmos de íArvore de Decisão. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 21. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 179-190.
DOI: https://doi.org/10.5753/wscad.2020.14068.
