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Advanced high performance algorithms for data processing. / Bogdanov, Alexander V.; Boukhanovsky, Alexander V.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Marian Bubak; Geert Dick van Albada; Peter M.A. Sloot; Jack J. Dongarra. Springer Nature, 2004. p. 239-246 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3036).

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Harvard

Bogdanov, AV & Boukhanovsky, AV 2004, Advanced high performance algorithms for data processing. in M Bubak, GD van Albada, PMA Sloot & JJ Dongarra (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3036, Springer Nature, pp. 239-246. https://doi.org/10.1007/978-3-540-24685-5_30

APA

Bogdanov, A. V., & Boukhanovsky, A. V. (2004). Advanced high performance algorithms for data processing. In M. Bubak, G. D. van Albada, P. M. A. Sloot, & J. J. Dongarra (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 239-246). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3036). Springer Nature. https://doi.org/10.1007/978-3-540-24685-5_30

Vancouver

Bogdanov AV, Boukhanovsky AV. Advanced high performance algorithms for data processing. In Bubak M, van Albada GD, Sloot PMA, Dongarra JJ, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Nature. 2004. p. 239-246. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-24685-5_30

Author

Bogdanov, Alexander V. ; Boukhanovsky, Alexander V. / Advanced high performance algorithms for data processing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Marian Bubak ; Geert Dick van Albada ; Peter M.A. Sloot ; Jack J. Dongarra. Springer Nature, 2004. pp. 239-246 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

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title = "Advanced high performance algorithms for data processing",
abstract = "We analyze the problem of processing of very large datasets on parallel systems and find that the natural approaches to parallelization fail for two reasons. One is connected to long-range correlations between data and the other comes from nonscalar nature of the data. To overcome those difficulties the new paradigm of the data processing is proposed, based on a statistical simulation of the datasets, which in its turn for different types of data is realized on three approaches - decomposition of the statistical ensemble, decomposition on the base of principle of mixing and decomposition over the indexing variable. Some examples of proposed approach show its very effective scaling.",
author = "Bogdanov, {Alexander V.} and Boukhanovsky, {Alexander V.}",
note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2004",
doi = "10.1007/978-3-540-24685-5_30",
language = "English",
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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
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RIS

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AB - We analyze the problem of processing of very large datasets on parallel systems and find that the natural approaches to parallelization fail for two reasons. One is connected to long-range correlations between data and the other comes from nonscalar nature of the data. To overcome those difficulties the new paradigm of the data processing is proposed, based on a statistical simulation of the datasets, which in its turn for different types of data is realized on three approaches - decomposition of the statistical ensemble, decomposition on the base of principle of mixing and decomposition over the indexing variable. Some examples of proposed approach show its very effective scaling.

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