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

Translated title of the contribution: Improved runtime performance of compression algorithms on CPU and GPU using CUDA

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Translated title of the contributionImproved runtime performance of compression algorithms on CPU and GPU using CUDA
Original languageSpanish
Title of host publicationProceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
PublisherLatin American and Caribbean Consortium of Engineering Institutions
ISBN (Electronic)9780999344316
DOIs
StatePublished - 2018
Event16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology - Lima, Peru
Duration: 18 Jul 201820 Jul 2018

Publication series

NameProceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
Volume2018-July
ISSN (Electronic)2414-6390

Conference

Conference16th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
Country/TerritoryPeru
CityLima
Period18/07/1820/07/18

Bibliographical note

Funding Information:
En este trabajo se utilizó CUDA, por la fiabilidad de las herramientas y la disponibilidad de hardware de NVIDIA. Además se utilizó CUDA Toolkit 8.0 [15], que proporciona un entorno de desarrollo integral para desarrolladores de C y C ++ que crean aplicaciones aceleradas por GPU, incluye un compilador para GPUs NVIDIA, bibliotecas matemáticas y herramientas para depurar y optimizar el rendimiento de las aplicaciones. Para la paralelización del algoritmo LZ78, se utilizó un estándar OpenACC (Para Aceleradores Abiertos) el cual es un estándar de programación para la informática paralela desarrollada por Cray, CAPS, NVIDIA y PGI. El estándar está diseñado para simplificar la programación paralela de sistemas heterogéneos de CPU/GPU [16].

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

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