ListarGI-GAC - Congresos, conferencias, etc. por tema "GPU"

Mostrando ítems 1-5 de 5

    • Free adaptive tessellation strategy of bézier surfaces 

      Concheiro, Raquel; Amor, Margarita; Bóo, Montserrat; Padrón, Emilio J. (SciTePress, 2014-01)
      [Abstract] Rendering of Bézier surfaces is currently performed by tessellating the model on the GPU and rendering the highly detailed triangle mesh. Whereas non-adaptive strategies apply the same tessellation pattern to ...

    • Probing the Efficacy of Hardware-Aware Weight Pruning to Optimize the SpMM routine on Ampere GPUs 

      López Castro, Roberto; Andrade, Diego; Fraguela, Basilio B. (Institute of Electrical and Electronics Engineers, 2022)
      [Abstract]: The Deep Learning (DL) community found in pruning techniques a good way to reduce the models' resource and energy consumption. These techniques lead to smaller sparse models, but sparse computations in GPUs ...

    • Synthesis of Multiresolution Scenes with Global Illumination on a GPU 

      Concheiro, Raquel; Amor, Margarita; Bóo, Montserrat; Iglesias, Iago; Padrón, Emilio J.; Doallo, Ramón (SciTePress, 2012-02)
      [Abstract] The radiosity computation has the important feature of producing view independent results, but these results are mesh dependent and, in consequence, are attached to a specific level of detail in the input mesh. ...

    • Texture Mapping on NURBS Surface 

      Vázquez Pardo, Sergio; Amor, Margarita (M D P I AG, 2018-09-17)
      [Abstract] Texture mapping allows high resolution details over 3D surfaces. Nevertheless, texture mapping has a number of unresolved problems such as distortion, boundary between textures or filtering. On the other hand, ...

    • VENOM: A Vectorized N:M Format for Unleashing the Power of Sparse Tensor Cores 

      López Castro, Roberto; Ivanov, Andrei; Andrade, Diego; Ben-Nun, Tal; Fraguela, Basilio B.; Hoefler, Torsten (Association for Computing Machinery, 2023-11)
      [Abstract]: The increasing success and scaling of Deep Learning models demands higher computational efficiency and power. Sparsification can lead to both smaller models as well as higher compute efficiency, and accelerated ...