Lempel–Ziv Data Compression on Parallel and Distributed Systems

Agostino, Sergio De

doi:10.3390/a4030183

Cited by 26 publications

(5 citation statements)

References 37 publications

(72 reference statements)

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“…LZW is used to compress text, executable code, and similar data files to about one-half their original size. A higher compression of 1:5 is also possible as reported in [5] Run Length Encoding is data coding with frequently repeated characters. It is called run-length because a run is made for repeated bits and coded in lesser bits by only stating how many bits were there [6].…”

Section: Previous Workmentioning

confidence: 99%

Healthcare Cloud Integration using Distributed Cloud Storage and Hybrid Image Compression

Hussein¹,

Badr²

2013

IJCA

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In recent years, the development and demand of multimedia product grows increasingly fast, contributing to insufficient bandwidth of network and storage of memory device. Therefore, the theory of data compression became more significant for reducing data redundancy to save more hardware space and transmission bandwidth. Cloud computing on the other hand; provides elastic services, high performance and scalable data storage to a large and everyday increasing number of healthcare users. Today, clouds are mainly used for handling highly intensive computing workloads and for providing very large data storage facilities. Both goals are combined with a third goal of potentially reducing healthcare data storage cost. In this research, distributed cloud storage that can interact with many cloud providers was used as a backend while hybrid image compression/decompression technique was used in the front end.

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Section: Previous Workmentioning

confidence: 99%

Healthcare Cloud Integration using Distributed Cloud Storage and Hybrid Image Compression

Hussein¹,

Badr²

2013

IJCA

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“…As we are interested in parallel implementations, we focus on LZ77 in this work rather than LZ78, since LZ77 admits efficient parallel solutions, whereas LZ78 was shown to be P-complete (unlikely to have an efficient parallel solution) [10,11].…”

Section: Introductionmentioning

confidence: 99%

Practical Parallel Lempel-Ziv Factorization

Shun

Zhao

2013

2013 Data Compression Conference

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In the age of big data, the need for efficient data compression algorithms has grown. A widely used data compression method is the Lempel-Ziv-77 (LZ77) method, being a subroutine in popular compression packages such as gzip and PKZIP. There has been a lot of recent effort on developing practical sequential algorithms for Lempel-Ziv factorization (equivalent to LZ77 compression), but research in practical parallel implementations has been less satisfactory. In this work, we present a simple work-efficient parallel algorithm for Lempel-Ziv factorization. We show theoretically that our algorithm requires linear work and runs in O(log 2 n) time (randomized) for constant alphabets and O(n ) time ( < 1) for integer alphabets. We present experimental results showing that our algorithm is efficient and achieves good speedup with respect to the best sequential implementations of Lempel-Ziv factorization.

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“…On the other hand, zipped files do not have such advantage, since they are compressed by the Lempel-Ziv sliding window technique [2,12,13]. In such cases, both coding and decoding are parallelizable in a symmetric way, requiring at least logarithmic time in practice [8,14].…”

Section: Introductionmentioning

confidence: 99%

“…The pair compressor/decompressor presents an asymmetry with respect to global parallel computation, since the encoder is not parallelizable [7,8], while the decoder has a very efficient parallelization [8][9][10]. On realistic data, the number of iterations of the decoding algorithm is much less than ten units when expressed on the parallel random access shared memory machine [10], and about ten units when expressed in the MapReduce parallel programming paradigm [11], as we will show in this paper.…”

Section: Introductionmentioning

confidence: 99%

Concurrent vs. Exclusive Reading in Parallel Decoding of LZ-Compressed Files

2017

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Broadcasting a message from one to many processors in a network corresponds to concurrent reading on a random access shared memory parallel machine. Computing the trees of a forest, the level of each node in its tree and the path between two nodes are problems that can easily be solved with concurrent reading in a time logarithmic in the maximum height of a tree. Solving such problems with exclusive reading requires a time logarithmic in the number of nodes, implying message passing between disjoint pairs of processors on a distributed system. Allowing concurrent reading in parallel algorithm design for distributed computing might be advantageous in practice if these problems are faced on shallow trees with some specific constraints. We show an application to LZC (Lempel-Ziv-Compress)-compressed file decoding, whose parallelization employs these computations on such trees for realistic data. On the other hand, zipped files do not have this advantage, since they are compressed by the Lempel-Ziv sliding window technique.

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Lempel–Ziv Data Compression on Parallel and Distributed Systems

Cited by 26 publications

References 37 publications

Healthcare Cloud Integration using Distributed Cloud Storage and Hybrid Image Compression

Healthcare Cloud Integration using Distributed Cloud Storage and Hybrid Image Compression

Practical Parallel Lempel-Ziv Factorization

Concurrent vs. Exclusive Reading in Parallel Decoding of LZ-Compressed Files

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