Vector nonlinear time-series analysis of gamma-ray burst datasets on heterogeneous clusters
Scientific Programming(2005)
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.
MoreTranslated text
Key words
related datasets,single statistical model,parameterized statistical model,gamma-ray burst datasets,vector nonlinear time-series analysis,efficient execution,general purpose linux cluster,heterogeneous cluster,efficient communication,cluster environment,multiple datasets,statistical computing,statistical model,goodness of fit,heterogeneous computing,data analysis,linux cluster,load balance,autoregressive model,gamma ray burst
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined