ISOBAR preconditioner for effective and high-throughput lossless data compression

Eric R. Schendel, Ye Jin, Neil Shah, Jackie Chen, C. S. Chang, Seung Hoe Ku, Stephane Ethier, Scott Klasky, Robert Latham, Robert Ross, Nagiza F. Samatova

Research output: Contribution to journalArticle

50 Citations (Scopus)

Abstract

Efficient handling of large volumes of data is a necessity for exascale scientific applications and database systems. To address the growing imbalance between the amount of available storage and the amount of data being produced by high speed (FLOPS) processors on the system, data must be compressed to reduce the total amount of data placed on the file systems. General-purpose loss less compression frameworks, such as zlib and bzlib2, are commonly used on datasets requiring loss less compression. Quite often, however, many scientific data sets compress poorly, referred to as hard-to-compress datasets, due to the negative impact of highly entropic content represented within the data. An important problem in better loss less data compression is to identify the hard-to-compress information and subsequently optimize the compression techniques at the byte-level. To address this challenge, we introduce the In-Situ Orthogonal Byte Aggregate Reduction Compression (ISOBAR-compress) methodology as a preconditioner of loss less compression to identify and optimize the compression efficiency and throughput of hard-to-compress datasets.

Original languageEnglish
Article number6228079
Pages (from-to)138-149
Number of pages12
JournalProceedings - International Conference on Data Engineering
DOIs
Publication statusPublished - 30 Jul 2012

    Fingerprint

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Software

Cite this

Schendel, E. R., Jin, Y., Shah, N., Chen, J., Chang, C. S., Ku, S. H., Ethier, S., Klasky, S., Latham, R., Ross, R., & Samatova, N. F. (2012). ISOBAR preconditioner for effective and high-throughput lossless data compression. Proceedings - International Conference on Data Engineering, 138-149. [6228079]. https://doi.org/10.1109/ICDE.2012.114