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Parallel data compression
Data compression schemes remove data redundancy in communicated and stored data and increase the effective capacities of communication and storage devices. Parallel algorithms and implementations for textual data compression are surveyed. Related concepts from parallel computation and information theory are briefly discussed. Static and dynamic methods for codeword construction and transmission on various models of parallel computation are described. Included are parallel methods which boost system speed by coding data concurrently, and approaches which employ multiple compression techniques to improve compression ratios. Theoretical and empirical comparisons are reported and areas for future research are suggested
Parallel Recursive State Compression for Free
This paper focuses on reducing memory usage in enumerative model checking,
while maintaining the multi-core scalability obtained in earlier work. We
present a tree-based multi-core compression method, which works by leveraging
sharing among sub-vectors of state vectors.
An algorithmic analysis of both worst-case and optimal compression ratios
shows the potential to compress even large states to a small constant on
average (8 bytes). Our experiments demonstrate that this holds up in practice:
the median compression ratio of 279 measured experiments is within 17% of the
optimum for tree compression, and five times better than the median compression
ratio of SPIN's COLLAPSE compression.
Our algorithms are implemented in the LTSmin tool, and our experiments show
that for model checking, multi-core tree compression pays its own way: it comes
virtually without overhead compared to the fastest hash table-based methods.Comment: 19 page
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