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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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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