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(More)
We present an asynchronous Quasi-Monte Carlo (qmc) algorithm 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 (mc). Our algorithm focused on the asynchronous computation of randomized lattice (Korobov) rules. Whereas(More)
This paper addresses the design of distributed methods which incorporate numerical extrapolation into adaptive multivariate integration, in order to increase the function-ality 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(More)
We study the effect of irregular function behavior and dynamic task partitioning on the parallel performance of the adaptive mul-tivariate 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)
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