Vector nonlinear time-series analysis of gamma-ray burst datasets on heterogeneous clusters

Authors:
Ioana Banicescu;Ricolindo L. Cariòo;Jane L. Harvill;John Patrick Lestrade
Affiliations:
Dept. of Comp. Sci. and Eng., PO Box 9637, Mississippi Ste. Univ., MS 39762, USA. Email: ioana@cse.msstate.edu and Ctr. for Comp. Sci. ERC, Mississippi Ste. Univ., PO Box 9627, Mississippi Ste. Un ...;Center for Computational Sciences ERC, Mississippi State University, PO Box 9627, Mississippi State University, Mississippi State MS 39762, USA. E-mail: rlc@erc.msstate.edu;Ctr. for Computnl. Sciences ERC, Mississippi Ste. Univ., PO Box 9627, Mississippi State University, rlc@erc.msstate.edu and Dept. of Math. and Stats., Mississippi Ste. Univ., Mississippi Ste. MS 3 ...;Department of Physics and Astronomy, Mississippi State University, PO Box 5167, Mississippi State University, Mississippi State MS 39762, USA. E-mail: lestrade@ra.msstate.edu
Venue:
Scientific Programming - International Symposium of Parallel and Distributed Computing & International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogenous Networks
Year:
2005

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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.