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Performance analysis of data compression algorithms for heterogeneous architecture through parallel approach

  • Farooq Sunar Mahammad
  • V. Madhu Viswanatham
Article
  • 28 Downloads

Abstract

Today, there is a huge demand for data compression due to the need to reduce the transmission time and increase the capacity of data storage. Data compression is a technique which represents an information, images, video files in a compressed or in a compact format. There are various data compression techniques which keep information as accurately as possible with the fewest number of bits and send it through communication channel. Arithmetic algorithm, Lempel–Ziv 77 (LZ77) and run length encoding with a K-precision (K-RLE) algorithms are lossless data compression algorithms which have lower performance rate because of their processing complexity as well as execution time. This paper presents an efficient parallel approach to reduce execution time for compression algorithms. The proposed OpenMP is an efficient tool for programming within parallel shared-memory environments. Finally, it shows that performance parallel model experimented using Open Multi-Processing (OpenMP) Application Programming Interface through Intel Parallel studio on multicore architecture platform with spec of Core 2 duo—2.4 GHz, 1 Gb RAM machine of parallel approach for compression algorithms has been improved remarkably against sequential approach. The improvement in compression ratio through an efficient parallel approach leads to reduction on transmission cost, reduction in storage space and bandwidth without additional hardware infrastructure. An overall performance evaluation shows arithmetic data compression algorithm with 46% which is better than LZ77 of 44% as well as K-RLE of 37% data compression algorithms.

Keywords

Arithmetic coding Data compression K-RLE algorithm LZ77 algorithm Multicore architecture Parallel processing OpenMP 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computing Science and EngineeringVIT UniversityVelloreIndia

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