Uma metodologia para a avaliação de desempenho e custos do treinamento de redes neurais em ambientes de nuvem
Resumo
Redes neurais profundas são soluções para problemas que envolvem reconhecimento de padrões e diversos trabalhos tentam encontrar maneiras de otimizar o desempenho dessas redes. Essa otimização necessita de hardware adequado para ser implementada, hardware esse que pode ser muito custoso para pequenas e médias organizações. O objetivo deste trabalho é propor uma metodologia para avaliar o desempenho e custo do treinamento de redes neurais convolucionais, considerando os fatores mais impactantes no tempo de treinamento e avaliar o custo financeiro total do ambiente para essa tarefa. Nesse sentido, observou-se que fatores como o tamanho da imagem de entrada e a arquitetura da rede tem grande impacto na métrica de tempo de treinamento e por consequência no custo total.Referências
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CARNEIRO, T. et al. Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications. IEEE Access, v. 6, p. 61677–61685, 2018.
CORREIA, Fernando. Definição de computação em nuvem segundo o NIST. Plataforma Nuvem, 2011. Disponível em: [link]. Acesso em: 02/07/2023.
EDINAT A. Cloud Computing Pricing Models: A Survey. International Journal of Scientific Engineering and Research (IJSER). 2018.
ELSHAWI, R. et al. DLBench: a comprehensive experimental evaluation of deep learning frameworks. Cluster Computing, v. 24, n. 3, p. 2017–2038, 7 fev. 2021.
JACKSON, K. R. et al. Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud. 2010 IEEE Second International Conference on Cloud Computing Technology and Science, nov. 2010.
JUVE, G. et al. Scientific workflow applications on Amazon EC2. 16 maio 2010.
KANSAL, S. et al. Pricing Models in Cloud Computing. Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies, 27 out. 2014.
KRIZHEVSKY, A. 2009. CIFAR-10 and CIFAR-100 datasets. Disponível em: [link]. Acesso em: 20/02/2024.
LECUN, Y., e CORTES, C. (2005). The mnist database of handwritten digits.
LECUN, Y.; BENGIO, Y.; HINTON, G. Deep Learning. Nature, v. 521, n. 7553, p. 436–444, maio 2015.
LILJA, D. J. Measuring Computer Performance: a practitioner’s guide. [S.l.]: Cambridge University Press, 2005. 278p.
LIU, J. et al. “Usability Study of Distributed Deep Learning Frameworks For Convolutional Neural Networks.” 2018.
MEETU KANDPAL; GAHLAWAT, M.; PATEL, K. R. Role of predictive modeling in cloud services pricing: A survey. 1 jan. 2017.
MENASCÉ, D. A.; ALMEIDA, V. A. F. Performance by Design: computer capacity planning by example. [S.l.]: Prentice Hall PTR, 2005. 462p.
OpenAI Five. 2018. Disponível em: [link]. Acesso em: 20/02/2024.
SHAMS, S. et al. Evaluation of Deep Learning Frameworks Over Different HPC Architectures. 1 jun. 2017.
SHI, S. et al. Benchmarking State-of-the-Art Deep Learning Software Tools. arXiv (Cornell University), 25 ago. 2016.
SIMONYAN, K. e ZISSERMAN, A. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” CoRR abs/1409.1556 (2014): n. pag.
WU, C.; BUYYA, R.; RAMAMOHANARAO, K. Cloud Pricing Models. ACM Computing Surveys, v. 52, n. 6, p. 1–36, 21 jan. 2020.
WU, Y. et al. A Comparative Measurement Study of Deep Learning as a Service Framework. IEEE Transactions on Services Computing, p. 1–1, 2019.
XIE, X. et al. Performance Evaluation and Analysis of Deep Learning Frameworks. 23 set. 2022.
ZHU, H. et al. TBD: Benchmarking and Analyzing Deep Neural Network Training. arXiv (Cornell University), 16 mar. 2018.
Publicado
21/07/2024
Como Citar
MOURA FILHO, Cláudio Márcio de Araújo; SOUZA, Érica Teixeira Gomes de.
Uma metodologia para a avaliação de desempenho e custos do treinamento de redes neurais em ambientes de nuvem. In: WORKSHOP EM DESEMPENHO DE SISTEMAS COMPUTACIONAIS E DE COMUNICAÇÃO (WPERFORMANCE), 23. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 1-12.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2024.1986.
