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)
We examine current paradigms in parallel strategies for multi-variate 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)
This is a document created when the latest version of mpich was installed (version 1.1.2) in July of 1999. It attempts to make the process of beginning to use mpich here at wmu a little smoother, and also brieey explains some more advanced topics, like using debuggers and the logger. Note that it does not explain how to program in mpi itself. This document(More)
The adaptive integration algorithm is eeective in numerically solving integration problems. It is able to focus the application of integration rules on the portion of the integration region where the integrand is the least well-behaved. Parallel implementations must use dynamic load balancing or performance suuers. Dynamic local load-balancing techniques(More)
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