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    • Efficiency of GPUs for Relational Database Engine ProcessingPeer reviewedClosed access 

      2016, CREMER, Samuel; Bagein, Michel; Mahmoudi, Saïd; Manneback, Pierre, HE en HainautAutre
      Article scientifique
      Relational database management systems (RDBMS) are still widely required by numerous business applications. Boosting performances without compromising functionalities represents a big challenge. To achieve this goal, we propose to boost an existing RDBMS by making it able to use hardware architectures with high memory bandwidth like GPUs. In this paper we present a solution named CuDB. We compare ...

    • Improving Performances of an Embedded Relational Database Management System with a Hybrid CPU/GPU Processing EnginePeer reviewedClosed access 

      2017, CREMER, Samuel; Bagein, Michel; Mahmoudi, Saïd; Manneback, Pierre, HE en HainautAutre
      Article scientifique
      End-user systems are increasingly impacted by the exponential growth of data volumes and their processing. Moreover, post-processing operations, essentially dedicated to ergonomic features, require more and more resources. Improving overall performances of embedded relational database management systems (RDBMS) can contribute to deliver better responsiveness of end-user systems while increasing the ...

    • 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 ...