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Title: Technical Report: Scalable Parallel Algorithms for High Dimensional Numerical Integration

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.
Authors:
 [1] ;  [2]
  1. Universidad del Turabo
  2. ORNL
Publication Date:
OSTI Identifier:
990238
Report Number(s):
ORNL/TM-2010/150
TRN: US201020%%465
DOE Contract Number:
DE-AC05-00OR22725
Resource Type:
Technical Report
Research Org:
Oak Ridge National Laboratory (ORNL); Spallation Neutron Source; Center for Computational Sciences
Sponsoring Org:
ORNL LDRD Director's R&D
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICAL METHODS AND COMPUTING; ALGORITHMS; PARALLEL PROCESSING; PERFORMANCE; MONTE CARLO METHOD; DATA PROCESSING