We introduce pseudo-prefix dictionaries, which allow an approximation scheme for optimal compression on a distibuted system with an improvement on previous implementations.
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The greedy approach to dictionary-based static text compression can be executed by a finite-state machine. When it is applied in parallel to different blocks of data independently, there is no lack of robustness even on standard large... more
The greedy approach to dictionary-based static text compression can be executed by a finite-state machine. When it is applied in parallel to different blocks of data independently, there is no lack of robustness even on standard large scale distributed systems with input files of arbitrary size. Beyond standard large scale, a negative effect on the compression effectiveness is caused by the very small size of the data blocks. A robust approach for extreme distributed systems is presented in this paper, where this problem is fixed by overlapping adjacent blocks and preprocessing the neighborhoods of the boundaries.
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The LZ2 compression method seems hardly parallelizable since some related heuristics are known to be P-complete. In spite of such negative result, the decoding process can be parallelized efficiently for the next character heuristic. We... more
The LZ2 compression method seems hardly parallelizable since some related heuristics are known to be P-complete. In spite of such negative result, the decoding process can be parallelized efficiently for the next character heuristic. We show an other parallel decoding algorithm for LZ2 compression using the ID update heuristic. The algorithm works in O(log2n) time with O(n/log(n)) processors on a
ABSTRACT We present a method for compressing binary images via monochromatic pattern substitution. Such method has no relevant loss of compression effectiveness if the image is partitioned into up to a thousand blocks, approximately, and... more
ABSTRACT We present a method for compressing binary images via monochromatic pattern substitution. Such method has no relevant loss of compression effectiveness if the image is partitioned into up to a thousand blocks, approximately, and each block is compressed independently. Therefore, it can be implemented on a distributed system with no interprocessor communication. In the theoretical context of unbounded parallelism, interprocessor communication is needed. Compression effectiveness has a bell-shaped behaviour which is again competitive with the sequential performance when the highest degree of parallelism is reached. Finally, the method has a speed-up if applied sequentially to an image partitioned into up to 256 blocks. It follows that such speed-up can be applied to a parallel implementation on a small scale system.
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We show nearly work-optimal parallel decoding algorithms which run on the PRAM EREW in O ( log n) time with O (n/( log n)1/2) processors for text compressed with LZ1 and LZ2 methods, where n is the length of the output string. We also... more
We show nearly work-optimal parallel decoding algorithms which run on the PRAM EREW in O ( log n) time with O (n/( log n)1/2) processors for text compressed with LZ1 and LZ2 methods, where n is the length of the output string. We also present pseudo work-optimal PRAM EREW decoders for finite window compression and LZ2 compression requiring logarithmic time with O (dn) work, where d is the window size and the alphabet size respectively. Finally, we observe that PRAM EREW decoders requiring O ( log n) time and O (n/ log n) processors are possible with the non-conservative assumption that the computer word length is O ( log 2 n) bits.
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The LZ2 compression method seems hardly parallelizable since some related heuristics are known to be P-complete. In spite of such a negative result, the algorithm process can be parallelized efficiently. In this paper, we show a... more
The LZ2 compression method seems hardly parallelizable since some related heuristics are known to be P-complete. In spite of such a negative result, the algorithm process can be parallelized efficiently. In this paper, we show a work-optimal parallel decoding Las Vegas algorithm for text compressed by standard implementation of the LZ2 algorithm (next character heuristic). The algorithm works in expected logarithmic time on a PRAM CRCW. We also address a different implementation called the identity heuristic. In this case we need to make the realistic assumption that the length of the dictionary elements is logarithmic in order to decode with optimal parallel work. The algorithm takes deterministic logarithmic time on a PRAM CREW
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While sliding window (LZ1) compression can be parallelized efficiently, the LZ2 compression method seems hardly parallelizable since some related heuristics are known to be P-complete. In spite of such negative result, there are parallel... more
While sliding window (LZ1) compression can be parallelized efficiently, the LZ2 compression method seems hardly parallelizable since some related heuristics are known to be P-complete. In spite of such negative result, there are parallel decoders which run in O(log2 n) time with O(n/log n) processors on the PRAM EREW where n is the length of the output string, as for LZ1 decompression. We show a faster parallel decoding algorithm which runs on the PRAM EREW in O(log n) time with O(n) processors for text compressed by a standard implementation of the LZ2 algorithm (next character heuristic). We observe that LZ1 parallel decoders also can have such speed up. Moreover, we address a different implementation of LZ2 compression called identity heuristic. In this case, decoding on the PRAM EREW takes O(log n log log n) time with O(n/log n) processors with the realistic assumption that the length of the dictionary elements is logarithmic
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A system of asynchronous parallel processes is represented by an exclusion graph in which a vertex is a process and an edge is a pair of mutually excluding processes. The mutual exclusion problem can be managed by simple entrance and exit... more
A system of asynchronous parallel processes is represented by an exclusion graph in which a vertex is a process and an edge is a pair of mutually excluding processes. The mutual exclusion problem can be managed by simple entrance and exit protocols using PVchunk operations on a single shared variable when the graph is a threshold one. Ordman wonders if an efficient way exists for managing mutual exclusion situations modeled by more complex graphs than the threshold ones. A start is made on that here: we present a solution of the problem when the model is in the class of matrogenic graphs, which properly contains the threshold graphs.
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The unbounded version of the Lempel-Ziv dynamic dictionary compression method is P-complete. Therefore, it is unlikely to implement it with sublinear work space unless a deletion heuristic is applied to bound the dictionary. The... more
The unbounded version of the Lempel-Ziv dynamic dictionary compression method is P-complete. Therefore, it is unlikely to implement it with sublinear work space unless a deletion heuristic is applied to bound the dictionary. The well-known LRU (least recently used) strategy provides the best compression performance among the existent deletion heuristics. We show experimental results on the compression effectiveness of a relaxed version (RLRUp) of the LRU heuristic. RLRUp partitions the dictionary in p equivalence classes, so that all the elements in each class are considered to have the same…
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In this paper three parallel recognition algorithms for threshold, matrogenic and box-threshold graphs, respectively, are given. These classes of graphs are inclusionwise comparable and depend only on their degree sequences. The... more
In this paper three parallel recognition algorithms for threshold, matrogenic and box-threshold graphs, respectively, are given. These classes of graphs are inclusionwise comparable and depend only on their degree sequences. The algorithms run in O(log n) parallel time on a PRAM-EREW model of computation and require O(n/log n) processors when the degree sequence, ordered in decreasing fashion, is given as input.
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Sheinwald, Lempel, and Ziv (1995,Inform. and Comput.116, 128–133) proved that the power of off-line coding is not useful if we want on-line decodable files, as far as asymptotical results are concerned. In this paper, we are concerned... more
Sheinwald, Lempel, and Ziv (1995,Inform. and Comput.116, 128–133) proved that the power of off-line coding is not useful if we want on-line decodable files, as far as asymptotical results are concerned. In this paper, we are concerned with the finite case and consider the notion of on-line decodable optimal parsing based on the parsing defined by the Ziv–Lempel (LZ2) compression algorithm. De Agostino and Storer (1996,Inform. Process. Lett.59, 169–174) proved the NP-completeness of computing the optimal parsing and that a sublogarithmic factor approximation algorithm cannot be realized on-line. We show that the Ziv–Lempel algorithm and two widely used practical implementations produce an O(n1/4) approximation of the optimal parsing, wherenis the length of the string. By working with de Bruijn sequences, we show also infinite families of binary strings on which the approximation factor isΘ(n1/4).
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Page 1. Data Granulation and Formal Concept Analysis Ray R. Hashemi', Sergio De Agostino',Bart Westgeest', and John R. Talbud ... 2003, Vol. 1, pp. 478-492. mining and formal concept analysis, Proceedings of the [SI R.... more
Page 1. Data Granulation and Formal Concept Analysis Ray R. Hashemi', Sergio De Agostino',Bart Westgeest', and John R. Talbud ... 2003, Vol. 1, pp. 478-492. mining and formal concept analysis, Proceedings of the [SI R. Wille, Restructuring lattice theory: An approach based ...
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Because of the size of information involved with the emerging applications in multimedia and the Human Genome Project, parallelism offers the only hope of meeting the challenges of storing such databases and searching through quickly. In... more
Because of the size of information involved with the emerging applications in multimedia and the Human Genome Project, parallelism offers the only hope of meeting the challenges of storing such databases and searching through quickly. In this paper, we address dictionary based lossless text compression and give the state-of-the-art in the field of parallelism. Static dictionary compression and sliding window
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Summary form only given. We show a parallel algorithm using a rectangle greedy matching technique which requires a linear number of processors and O (log (M) log (n)) time on the PRAM EREW model. The algorithm is suitable for practical... more
Summary form only given. We show a parallel algorithm using a rectangle greedy matching technique which requires a linear number of processors and O (log (M) log (n)) time on the PRAM EREW model. The algorithm is suitable for practical parallel ...
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- The bottom-up hierarchical clustering methodology that is introduced in this paper is an Extension of Self-organizing Map neural network (ESOM) and it provides remedy for two different major problems. The first one is related to the... more
- The bottom-up hierarchical clustering methodology that is introduced in this paper is an Extension of Self-organizing Map neural network (ESOM) and it provides remedy for two different major problems. The first one is related to the hierarchical clustering and the ...
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PRAM CREW parallel algorithms requiring logarithmic time and a linear number of processors exist for sliding (LZ1) and static dictionary compression. On the other hand, LZ2 compression seems hard to parallelize. Both adaptive methods work... more
PRAM CREW parallel algorithms requiring logarithmic time and a linear number of processors exist for sliding (LZ1) and static dictionary compression. On the other hand, LZ2 compression seems hard to parallelize. Both adaptive methods work with prefix dictionaries, that is, all ...
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In this paper, we show a simple lossless compression heuristic for color images in RGB format. The main advantage of this approach is that it provides a highly parallelizable compressor and decompressor. The lossless image compression... more
In this paper, we show a simple lossless compression heuristic for color images in RGB format. The main advantage of this approach is that it provides a highly parallelizable compressor and decompressor. The lossless image compression methods often consist of two distinct ...
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Abstract. A work-optimal O(log n log M) time PRAM-EREW algorithm for lossless image compression by block matching was shown in [1], where n is the size of the image and M is the maximum size of the match. The design of a parallel decoder... more
Abstract. A work-optimal O(log n log M) time PRAM-EREW algorithm for lossless image compression by block matching was shown in [1], where n is the size of the image and M is the maximum size of the match. The design of a parallel decoder was left as an open problem. By slightly ...
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Lossless Image Compression by Block Matching on Practical Massively Parallel Architectures Luigi Cinque and Sergio De Agostino Computer Science Department Sapienza University Via Salaria 113, 00198 Roma, Italy {cinque, deagostino}@ di.... more
Lossless Image Compression by Block Matching on Practical Massively Parallel Architectures Luigi Cinque and Sergio De Agostino Computer Science Department Sapienza University Via Salaria 113, 00198 Roma, Italy {cinque, deagostino}@ di. uniroma1. it Abstract. Work-...
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Speeding up Lossless Image Compression: Experimental Results on a Parallel Machine Luigi Cinque1, Sergio De Agostino1, and Luca Lombardi2 1 Computer Science Department Sapienza University Via Salaria 113, 00198 Roma, Italy {cinque,... more
Speeding up Lossless Image Compression: Experimental Results on a Parallel Machine Luigi Cinque1, Sergio De Agostino1, and Luca Lombardi2 1 Computer Science Department Sapienza University Via Salaria 113, 00198 Roma, Italy {cinque, deagostino}@ di. uniroma1. it 2 ...