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Scientific Programming
Volume 13 (2005), Issue 2, Pages 67-77
http://dx.doi.org/10.1155/2005/674158

Vector Nonlinear Time-Series Analysis of Gamma-Ray Burst Datasets on Heterogeneous Clusters

Ioana Banicescu,1,2 Ricolindo L. Cariño,2 Jane L. Harvill,2,3 and John Patrick Lestrade4

1Department of Computer Science and Engineering, PO Box 9637, Mississippi State University, Mississippi State MS 39762, USA
2Center for Computational Sciences ERC, Mississippi State University, PO Box 9627, Mississippi State University, Mississippi State MS 39762, USA
3Department of Mathematics and Statistics, Mississippi State University, PO Box MA, Mississippi State University, Mississippi State MS 39762, USA
4Department of Physics and Astronomy, Mississippi State University, PO Box 5167, Mississippi State University, Mississippi State MS 39762, USA

Received 1 December 2005; Accepted 1 December 2005

Copyright © 2005 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The simultaneous analysis of a number of related datasets using a single statistical model is an important problem in statistical computing. A parameterized statistical model is to be fitted on multiple datasets and tested for goodness of fit within a fixed analytical framework. Definitive conclusions are hopefully achieved by analyzing the datasets together. This paper proposes a strategy for the efficient execution of this type of analysis on heterogeneous clusters. Based on partitioning processors into groups for efficient communications and a dynamic loop scheduling approach for load balancing, the strategy addresses the variability of the computational loads of the datasets, as well as the unpredictable irregularities of the cluster environment. Results from preliminary tests of using this strategy to fit gamma-ray burst time profiles with vector functional coefficient autoregressive models on 64 processors of a general purpose Linux cluster demonstrate the effectiveness of the strategy.