A Flexible Research-Oriented Framework for Distributed Training of Deep Neural Networks. - Sergio Barrachina 001; Adrián Castelló 001; Mar Catalán; Manuel F. Dolz; José I. Mestre (IPDPS Workshops, 2021)

Performance Modeling for Distributed Training of Convolutional Neural Networks. - Adrián Castelló 001; Mar Catalán; Manuel F. Dolz; José I. Mestre; Enrique S. Quintana-Ortí; José Duato (PDP, 2021)

Evaluation of MPI Allreduce for Distributed Training of Convolutional Neural Networks. - Adrián Castelló 001; Mar Catalán; Manuel F. Dolz; José I. Mestre; Enrique S. Quintana-Ortí; José Duato (PDP, 2021)

PyDTNN: A user-friendly and extensible framework for distributed deep learning. - Sergio Barrachina 001; Adrián Castelló 001; Mar Catalán; Manuel F. Dolz; José I. Mestre (J. Supercomput., 2021)

Using machine learning to model the training scalability of convolutional neural networks on clusters of GPUs. - Sergio Barrachina 001; Adrián Castelló 001; Mar Catalán; Manuel F. Dolz; José I. Mestre (Computing, 2023)

Analyzing the impact of the MPI allreduce in distributed training of convolutional neural networks. - Adrián Castelló 001; Mar Catalán; Manuel F. Dolz; Enrique S. Quintana-Ortí; José Duato (Computing, 2023)

Análisis y modelización de los procesos de aprendizaje profundo - Mar Catalán (2024)