Rodger Zanny

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PARVIS is a visualization system for distributed, adaptive partitioning algorithms. It allows data–driven examination of the behavior of the adaptive algorithm, even for large and complex problems. For the algorithm developers it supports the analysis of load balancing techniques, subregion error patterns, rate of algorithm convergence for specific function(More)
We present an asynchronous Quasi-Monte Carlo (QMC) algorithm for numerical integration tailored for heterogeneous environments. QMC techniques are better suited for high dimensions than adaptive methods and have generally better convergence properties than classical Monte Carlo methods. The algorithm focuses on the asynchronous computation of randomized(More)
We study the effect of irregular function behavior and dynamic task partitioning on the parallel performance of the adaptive multivariate integration algorithm currently incorporated in ParInt. In view of the implicit hot spots in the computations, load balancing is essential to maintain parallel efficiency. A convergence model is given for a class of(More)
This paper addresses the design of distributed methods which incorporate numerical extrapolation into adaptive multivariate integration, in order to increase the functionality of the integration algorithms. When attempting to deal with singularities, adaptive integration algorithms need a very fine subdivision in the proximity of these “hot spots”. This is(More)
We examine current paradigms in parallel strategies for multivariate integration algorithms. These include various process structures (centralized vs. global) and work distribution strategies (static or dynamic) in synchronous or asynchronous implementations. The target algorithm classes are Monte Carlo, quasi-Monte Carlo and adaptive. Strengths and(More)
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