Technical Report: Scalable Parallel Algorithms for High Dimensional Numerical Integration

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We implemented a scalable parallel quasi-Monte Carlo numerical high-dimensional integration for tera-scale data points. The implemented algorithm uses the Sobol s quasi-sequences to generate random samples. Sobol s sequence was used to avoid clustering effects in the generated random samples and to produce low-discrepancy random samples which cover the entire integration domain. The performance of the algorithm was tested. Obtained results prove the scalability and accuracy of the implemented algorithms. The implemented algorithm could be used in different applications where a huge data volume is generated and numerical integration is required. We suggest using the hyprid MPI and OpenMP programming ... continued below

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Masalma, Yahya & Jiao, Yu October 1, 2010.

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We implemented a scalable parallel quasi-Monte Carlo numerical high-dimensional integration for tera-scale data points. The implemented algorithm uses the Sobol s quasi-sequences to generate random samples. Sobol s sequence was used to avoid clustering effects in the generated random samples and to produce low-discrepancy random samples which cover the entire integration domain. The performance of the algorithm was tested. Obtained results prove the scalability and accuracy of the implemented algorithms. The implemented algorithm could be used in different applications where a huge data volume is generated and numerical integration is required. We suggest using the hyprid MPI and OpenMP programming model to improve the performance of the algorithms. If the mixed model is used, attention should be paid to the scalability and accuracy.

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  • Report No.: ORNL/TM-2010/150
  • Grant Number: DE-AC05-00OR22725
  • DOI: 10.2172/990238 | External Link
  • Office of Scientific & Technical Information Report Number: 990238
  • Archival Resource Key: ark:/67531/metadc1014441

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  • October 1, 2010

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  • Oct. 14, 2017, 8:36 a.m.

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Masalma, Yahya & Jiao, Yu. Technical Report: Scalable Parallel Algorithms for High Dimensional Numerical Integration, report, October 1, 2010; United States. (digital.library.unt.edu/ark:/67531/metadc1014441/: accessed December 10, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.