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Content Provider | IEEE Xplore Digital Library |
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Author | Schendel, E.R. Ye Jin Shah, N. Chen, J. Chang, C.S. Seung-Hoe Ku Ethier, S. Klasky, S. Latham, R. Ross, R. Samatova, N.F. |
Copyright Year | 2012 |
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. |
Starting Page | 138 |
Ending Page | 149 |
File Size | 1093359 |
Page Count | 12 |
File Format | |
ISBN | 9781467300421 |
ISSN | 10844627 |
e-ISBN | 9780769547473 |
DOI | 10.1109/ICDE.2012.114 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-04-01 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Arrays Throughput Standards Data models Equations Probability distribution Noise |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Information Systems Software |
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