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Parallel-Distributed Implementation of the Lipizzaner Framework for Multiobjective Coevolutionary Training of Generative Adversarial Networks

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High Performance Computing (CARLA 2023)

Abstract

This article presents a parallel-distributed implementation of the Lipizzaner framework for multiobjective coevolutionary Generative Adversarial Networks training. A specific design is proposed following the messagge passing paradigm to execute in high performance computing infrastructures. The implementation is validated for the generation of handwritten digits problems. Accurate efficiency and scalability results, and a proper load balancing are reported.

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References

  1. Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862 (2017)

  2. Arora, S., Risteski, A., Zhang, Y.: Do GANs learn the distribution? some theory and empirics. In: International Conference on Learning Representations (2018)

    Google Scholar 

  3. Cardoso, R., Golubovic, D., Lozada, I.P., Rocha, R., Fernandes, J., Vallecorsa, S.: Accelerating GAN training using highly parallel hardware on public cloud. EPJ Web Conf. 251, 02073 (2021)

    Article  Google Scholar 

  4. Esteban, M., Toutouh, J., Nesmachnow, S.: Parallel/distributed intelligent hyperparameters search for generative artificial neural networks. In: Jagode, H., Anzt, H., Ltaief, H., Luszczek, P. (eds.) ISC High Performance 2021. LNCS, vol. 12761, pp. 297–313. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90539-2_20

    Chapter  Google Scholar 

  5. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  6. Gui, J., Sun, Z., Wen, Y., Tao, D., Ye, J.: A review on generative adversarial networks: algorithms, theory, and applications. IEEE Trans. Knowl. Data Eng. 35(4), 3313–3332 (2021)

    Article  Google Scholar 

  7. Hardy, C., Merrer, E.L., Sericola, B.: MD-GAN: multi-discriminator generative adversarial networks for distributed datasets. In: IEEE International Parallel and Distributed Processing Symposium (2019)

    Google Scholar 

  8. Liu, M., et al.: A decentralized parallel algorithm for training generative adversarial nets (2019). https://arxiv.org/abs/1910.12999

  9. Moran, N., Pollack, J.: Coevolutionary neural population models. In: Artificial Life Conference Proceedings, pp. 39–46. MIT Press One Rogers Street, Cambridge, MA 02142–1209, USA (2018)

    Google Scholar 

  10. Nesmachnow, S., Iturriaga, S.: Cluster-UY: collaborative scientific high performance computing in Uruguay. In: Torres, M., Klapp, J. (eds.) ISUM 2019. CCIS, vol. 1151, pp. 188–202. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-38043-4_16

    Chapter  Google Scholar 

  11. Perez, E., Nesmachnow, S., Toutouh, J., Hemberg, E., O’Reily, U.M.: Parallel/distributed implementation of cellular training for generative adversarial neural networks. In: IEEE International Parallel and Distributed Processing Symposium Workshops (2020)

    Google Scholar 

  12. Ripa, G., Mautone, A., Vidal, A., Nesmachnow, S., Toutouh, J.: Multiobjective coevolutionary training of generative adversarial networks. In: Genetic and Evolutionary Computation Conference (2023)

    Google Scholar 

  13. Schmiedlechner, T., Yong, N., Al-Dujaili, A., Hemberg, E., O’Reilly, U.: Lipizzaner: a system that scales robust generative adversarial network training (2018). https://arxiv.org/abs/1811.12843

  14. Toutouh, J., Esteban, M., Nesmachnow, S.: Parallel/distributed generative adversarial neural networks for data augmentation of COVID-19 training images. In: Nesmachnow, S., Castro, H., Tchernykh, A. (eds.) CARLA 2020. CCIS, vol. 1327, pp. 162–177. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68035-0_12

    Chapter  Google Scholar 

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Correspondence to Sergio Nesmachnow .

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Nesmachnow, S., Toutouh, J., Ripa, G., Mautone, A., Vidal, A. (2024). Parallel-Distributed Implementation of the Lipizzaner Framework for Multiobjective Coevolutionary Training of Generative Adversarial Networks. In: Barrios H., C.J., Rizzi, S., Meneses, E., Mocskos, E., Monsalve Diaz, J.M., Montoya, J. (eds) High Performance Computing. CARLA 2023. Communications in Computer and Information Science, vol 1887. Springer, Cham. https://doi.org/10.1007/978-3-031-52186-7_7

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  • DOI: https://doi.org/10.1007/978-3-031-52186-7_7

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  • Publisher Name: Springer, Cham

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