Mejora de rendimiento en tiempo de ejecución de los Algoritmos de Compresión en CPU y GPU utilizando CUDA

Milagros Mayta-Rosas, Henry Talavera-Díaz, Gonzalo Quispe-Huanca, Jose Alfredo Sulla Torres

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Currently users handle large amounts of data that are increasing, consequently the compression of these introduces an additional overhead and the performance of the hardware can be reduced, therefore must take into account the execution time as a key element to choose properly the algorithm perform this action. In this paper we present a parallel implementation of Lempel-Ziv (LZ78) and Run Length Encoding (RLE) algorithms, originally sequential, using the parallel programming model and Compute Unified Device Architecture (CUDA), on a NVIDIA-branded GPU device. It presents a comparison between the execution time of the algorithms in CPU and in GPU demonstrating a significant improvement in the execution time of the process of data compression on the GPU in comparison with the implementation based on the CPU in both algorithms.

Título traducido de la contribuciónImproved runtime performance of compression algorithms on CPU and GPU using CUDA
Idioma originalEspañol
Título de la publicación alojadaProceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
EditorialLatin American and Caribbean Consortium of Engineering Institutions
ISBN (versión digital)9780999344316
DOI
EstadoPublicada - 2018
Evento16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology - Lima, Perú
Duración: 18 jul. 201820 jul. 2018

Serie de la publicación

NombreProceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
Volumen2018-July
ISSN (versión digital)2414-6390

Conferencia

Conferencia16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
País/TerritorioPerú
CiudadLima
Período18/07/1820/07/18

Nota bibliográfica

Publisher Copyright:
© 2018 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.

Palabras clave

  • CUDA
  • GPU
  • LZ78
  • Lossless compression algorithms
  • Run Length Encoding

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