Vector nonlinear time-series analysis of gamma-ray burst datasets
on heterogeneous clusters
Issue title: International Symposium of Parallel and
Distributed Computing & International Workshop on Algorithms, Models and
Tools for Parallel Computing on Heterogenous Networks
Affiliations: Department of Computer Science and Engineering, PO Box
9637, Mississippi State University, Mississippi State MS 39762, USA. E-mail:
[email protected] | Center for Computational Sciences ERC, Mississippi
State University, PO Box 9627, Mississippi State University, Mississippi State
MS 39762, USA. E-mail: [email protected] | Department of Mathematics and Statistics, Mississippi
State University, PO Box MA, Mississippi State University, Mississippi State MS
39762, USA. E-mail: [email protected] | Department of Physics and Astronomy, Mississippi State
University, PO Box 5167, Mississippi State University, Mississippi State MS
39762, USA. E-mail: [email protected]
Note: [] Corresponding author
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.
Keywords: Heterogeneous computing, dynamic load balancing, data analysis
