Bieito
Beceiro Fernández

Research Publications

1. B. Beceiro, J. González-Domínguez, L. Morán-Fernández, V. Bolón-Canedo, and J. Touriño, "CUDA Acceleration of MI-based Feature Selection Methods," Journal of Parallel and Distributed Computing, vol. 190, Art. no. 104901, Aug. 2024. [Online]. Available: https://doi.org/10.1016/j.jpdc.2024.104901.
2. B. Beceiro, J. González-Domínguez, and J. Touriño, "Biblioteca Paralela de Selección de Características para Sistemas Clúster," in Actas de las Jornadas SARTECO 2022, Alacant, Spain, Sep. 2022, pp. 23–32. [Online]. Available: https://doi.org/10.5281/zenodo.7180809.
   Slides: jp22_slides.pdf [es]
3. B. Beceiro, J. González-Domínguez, and J. Touriño, "Parallel-FST: a Feature Selection Library for Multicore Clusters," Journal of Parallel and Distributed Computing, vol. 169, pp. 106–116, Nov. 2022. [Online]. Available: https://doi.org/10.1016/j.jpdc.2022.06.012.
4. B. Beceiro, J. González-Domínguez, and J. Touriño, "Acceleration of a Feature Selection Algorithm Using High Performance Computing," in Proceedings 3rd XoveTIC Conference, A Coruña, Spain, Sep. 2020, pp. 171–173. [Online]. Available: https://doi.org/10.3390/proceedings2020054054.
   Slides: xovetic20_slides.pdf [gl]

Software

• Parallel-FST: Feature Selection Library for Clusters

  
  Repository: parallel-fst
  Specific versions: jpdc22 [code, dockerhub], jpdc24 [code]

  Parallel-FST is a collection of feature selection libraries. Currently, it incorporates:

  • CLI-FEAST, a CLI wrapper for the original sequential methods of FEAST.
  • MPI-FEAST, a hybrid MPI/multithreaded implementation for multicore clusters (CLI application).
  • cuFEAST, a CUDA implementation for single-GPU nodes (C++ library and CLI application.

  You can find here some GIFs that show the feature selection process when applying MIM, JMI and mRMR to the MNIST dataset.

• pyCacheSimulator

  
  Repository: pycachesim
  PyPI package: pyCacheSimulator

  pyCacheSimulator is a Python library designed to simulate cache behavior and explore the effects of different configurations, replacement algorithms, and write policies. It allows to model multi-level cache hierarchies, track memory references, and analyze cache statistics, making it a valuable tool for both teaching and learning about computer architecture.

Short Bio

Bieito Beceiro received the B.S. in Computer Science and the M.S. in High Performance Computing (HPC) from the Universidade da Coruña (UDC), Spain, in 2020 and 2021, respectively.

He is currently a Ph.D. student at the Computer Architecture Group of the UDC. His work is focused on the acceleration of machine learning methods using HPC techniques.