GI-GAC - Artigos: Envíos recientes

Mostrando ítems 6-10 de 190

    • EPA-ng: Massively Parallel Evolutionary Placement of Genetic Sequences 

      Barbera, Pierre; Kozlov, Alexey M.; Czech, Lucas; Morel, Benoit; Darriba, Diego; Flouri, Tomas; Stamatakis, Alexandros (Oxford University Press, 2019-03-01)
      [Abstract]: Next generation sequencing (NGS) technologies have led to a ubiquity of molecular sequence data. This data avalanche is particularly challenging in metagenetics, which focuses on taxonomic identification of ...

    • Simulating the Network Activity of Modern Manycores 

      Horro, Marcos; Rodríguez, Gabriel; Touriño, Juan (Institute of Electrical and Electronics Engineers Inc., 2019)
      [Abstract]: Manycore architectures are one of the most promising candidates to reach the exascale. However, the increase in the number of cores on a single die exacerbates the memory wall problem. Modern manycore architectures ...

    • Robust step counting for inertial navigation with mobile phones 

      Rodríguez García, Germán; Casado, Fernando E.; Iglesias Rodríguez, Roberto; Regueiro, Carlos V.; Nieto, Adrián (MDPI AG, 2018-09-19)
      [Abstract]: Mobile phones are increasingly used for purposes that have nothing to do with phone calls or simple data transfers, and one such use is indoor inertial navigation. Nevertheless, the development of a standalone ...

    • STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep Learning 

      López Castro, Roberto; Andrade, Diego; Fraguela, Basilio B. (Institute of Electrical and Electronics Engineers, 2024-05)
      [Abstract]: The relentless growth of modern Machine Learning models has spurred the adoption of sparsification techniques to simplify their architectures and reduce the computational demands. Network pruning has demonstrated ...

    • CUDA acceleration of MI-based feature selection methods 

      Beceiro, Bieito; González-Domínguez, Jorge; Morán-Fernández, Laura; Bolón-Canedo, Verónica; Touriño, Juan (Elsevier, 2024-08)
      [Abstract]: Feature selection algorithms are necessary nowadays for machine learning as they are capable of removing irrelevant and redundant information to reduce the dimensionality of the data and improve the quality of ...