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    • Distributed Deep Learning: From Single-Node to Multi-Node ArchitecturePeer reviewedClosed access 

      2022, LERAT, Jean-Sébastien; Mahmoudi, Sidi Ahmed; Mahmoudi, Saïd, HE en Hainaut
      Article scientifique
      During the last years, deep learning (DL) models have been used in several applications with large datasets and complex models. These applications require methods to train models faster, such as distributed deep learning (DDL). This paper proposes an empirical approach aiming to measure the speedup of DDL achieved by using different parallelism strategies on the nodes. Local parallelism is considered ...

    • Single node deep learning frameworks: Comparative study and CPU/GPU performance analysisPeer reviewedClosed access 

      2021, LERAT, Jean-Sébastien; Mahmoudi, Sidi Ahmed; Mahmoudi, Saïd, HE en Hainaut
      Article scientifique
      Deep learning presents an efficient set of methods that allow learning from massive volumes of data using complex deep neural networks. To facilitate the design and implementation of algorithms, deep learning frameworks provide a high-level programming interface. Based on these frameworks, new models, and applications are able to make better and better predictions. One type of deep learning application ...