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Large Scale Parallel Numerical Integration
 Journal of Computational and Applied Mathematics
, 1999
"... We present and analyze strategies which can be used for the parallel computation of large numbers of integrals which may be of different levels of difficulty. Parallelization on the integral level, which is generally used for large numbers of integrals, is combined with parallelization on the subr ..."
Abstract

Cited by 7 (5 self)
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We present and analyze strategies which can be used for the parallel computation of large numbers of integrals which may be of different levels of difficulty. Parallelization on the integral level, which is generally used for large numbers of integrals, is combined with parallelization on the subregion level, which enables handling local integration difficulties within individual problems. This results in a new, hierarchical algorithm which incorporates load balancing on the integral level and on the subregion level. We report test results of the software and show that the hierarchical approach leads to a scalable integration algorithm. 1 Introduction If a large number of integrals need to be computed and a parallel machine or network based computing environment is available, the most natural approach is to divide the integrals uniformly among the processors (parallelization on the integral level), so that each of the p processors 1 hopefully contributes 1 p th of the total...
Integrand and Performance Analysis with PARINT / PARVIS
, 2000
"... We review the contents and capabilities of the parallel multivariate integration tool ParInt and its companion visualization tool ParVis. We also discuss current developments and applications, and focus on the analysis of both the integrand behavior and the parallel performance of the algorithms, fo ..."
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Cited by 2 (1 self)
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We review the contents and capabilities of the parallel multivariate integration tool ParInt and its companion visualization tool ParVis. We also discuss current developments and applications, and focus on the analysis of both the integrand behavior and the parallel performance of the algorithms, for example with respect to scalability. Current developments include asynchronous quasiMonte Carlo techniques for high dimensional integration, and applications to grid computing.