Avaliação do Consumo de Energia e o Impacto da emissão de CO2 para algoritmos de Inteligência Artificial
Resumo
Atualmente a Inteligencia Artificial (IA) é uma das forças mais transformadoras do nosso tempo, com resultados surpreendentes. Esses resultados se devem, em grande parte, ao uso de alta capacidade computacional oferecida pelos ambientes de HPC, os quais ao mesmo tempo requerem muita energia para seu funcionamento. Além disso, o consumo de energia é responsável pela emissão de gases de efeito estufa, entre os quais o CO2 é o mais expressivo. Neste trabalho é avaliado o impacto do treinamento de diferentes algoritmos de IA no consumo energético e na emissão de CO2 equivalente entre diferentes arquiteturas computacionais (ARM, GPU e X86).
Palavras-chave:
Inteligência Artificial, HPC, Energia, CO2
Referências
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.
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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 Goi ́as.
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.
Google (2019). Google 2019 environmental web report. https://sustainability.google/reports/environmental-report-2019.
Kloh, 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 efficiency of deep convolutional neural networks on cpus and gpus. In2016 IEEE International Conferences onBig Data and Cloud Computing, Social Computing and Networking, Sustainable Computingand 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 scientific 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 geraçãao de energia elétrica no Brasil: implicações da aplicação da Avaliação do Ciclo de Vida. PhD thesis, Universidade de São Paulo. ˜
Ould-Ahmed-Vall, E. (2017). Tensorflow optimizations on modern intel architecture. https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intelarchitecture.
Quinlan, J. R. (1996). Improved use of continuous attributes in c4.5. Journal of artificial intelligence research, 4:77–90.
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.
Santos, L. d. O. (2015). Caracterização de tarefas usando Redes Neurais e CUDA. PhD thesis, Universidade Estadual Paulista Julio de Mesquita Filho.
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.
Strubell, E., Ganesh, A., and McCallum, A. (2019). Energy and policy considerations fordeep learning in nlp. In Proceedings of the 57th Annual Meeting of the Association forComputational Linguistics, pages 3645–3650.
Wu, X., Taylor, V., Cook, J., and Mucci, P. J. (2016). Using performance-power modeling to improve energy efficiency of hpc applications. Computer, 49(10):20–29.
Yang, T.-J., Chen, Y.-H., and Sze, V. (2017). Designing energy-efficient convolutional neural networks using energy-aware pruning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5687–5695.
Asuncion, A. and Newman, D. (2007). UCI machine learning repository. https://archive.ics.uci.edu/ml/index.php
Avelar, V., Azevedo, D., French, A., and Power, E. N. (2012). Pue: a comprehensive examination of the metric. White paper, 49.
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 Goi ́as.
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.
Google (2019). Google 2019 environmental web report. https://sustainability.google/reports/environmental-report-2019.
Kloh, 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 efficiency of deep convolutional neural networks on cpus and gpus. In2016 IEEE International Conferences onBig Data and Cloud Computing, Social Computing and Networking, Sustainable Computingand 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 scientific 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 geraçãao de energia elétrica no Brasil: implicações da aplicação da Avaliação do Ciclo de Vida. PhD thesis, Universidade de São Paulo. ˜
Ould-Ahmed-Vall, E. (2017). Tensorflow optimizations on modern intel architecture. https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intelarchitecture.
Quinlan, J. R. (1996). Improved use of continuous attributes in c4.5. Journal of artificial intelligence research, 4:77–90.
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.
Santos, L. d. O. (2015). Caracterização de tarefas usando Redes Neurais e CUDA. PhD thesis, Universidade Estadual Paulista Julio de Mesquita Filho.
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.
Strubell, E., Ganesh, A., and McCallum, A. (2019). Energy and policy considerations fordeep learning in nlp. In Proceedings of the 57th Annual Meeting of the Association forComputational Linguistics, pages 3645–3650.
Wu, X., Taylor, V., Cook, J., and Mucci, P. J. (2016). Using performance-power modeling to improve energy efficiency of hpc applications. Computer, 49(10):20–29.
Yang, T.-J., Chen, Y.-H., and Sze, V. (2017). Designing energy-efficient convolutional neural networks using energy-aware pruning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5687–5695.
Publicado
30/06/2020
Como Citar
BERNARDO, Felipe; SILVA, Gabrieli; GRITZ, Matheus; FERRO, Mariza; SCHULZE, Bruno.
Avaliação do Consumo de Energia e o Impacto da emissão de CO2 para algoritmos de Inteligência Artificial. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 14. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 81-88.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2020.11185.
