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https://doi.org/10.1109/inpar....
Article . 2012 . Peer-reviewed
License: STM Policy #29
Data sources: Crossref
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Parallel lossless data compression on the GPU

Authors: Patel, Ritesh A; Zhang, Yao; Mak, Jason; Davidson, Andrew; Owens, John D;

Parallel lossless data compression on the GPU

Abstract

We present parallel algorithms and implementations of a bzip2-like lossless data compression scheme for GPU architectures. Our approach parallelizes three main stages in the bzip2 compression pipeline: Burrows-Wheeler transform (BWT), move-to-front transform (MTF), and Huffman coding. In particular, we utilize a two-level hierarchical sort for BWT, design a novel scan-based parallel MTF algorithm, and implement a parallel reduction scheme to build the Huffman tree. For each algorithm, we perform detailed performance analysis, discuss its strengths and weaknesses, and suggest future directions for improvements. Overall, our GPU implementation is dominated by BWT performance and is 2.78× slower than bzip2, with BWT and MTF-Huffman respectively 2.89× and 1.34× slower on average.

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
63
Top 10%
Top 10%
Top 10%
Green