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Utilizing distributed parallel computation for computer intensive statistical analysis within a heterogeneous computer environment

利用分布式并行计算在异构计算机环境中进行计算机密集型统计分析

基本信息

批准号：
15500189
负责人：
OCHI Yoshimichi
金额：
$ 1.66万
依托单位：
Oita University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2004
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15500189/
关键词：
distributed parallel computing MPI PVM task management computer intensive statistical analysis

项目摘要

We adopted a form of implementations of Beowulf. type PC cluster for the distributed parallel computation environment and studied the actual implementations and pros and cons of the use of such implementation. In order to install such system, we reorganized the network system and computer resource in our laboratories and established the distributed parallel computation environment with PVM and MPI(here we used "lam" as an actual implementation) with those computers.We developed a task management method "Time Prediction Model", which control tasks with their expected run time as a manager-worker model and the method was compared with "Work Pool Methods", in order to control tasks within our heterogeneous computing resource. Based on the results given here, we also considered a fundamental design of an integrated application development environment for the distributed parallel environment, which would support computationally intensive statistical investigation, such as Bootstrap and simulation based analyses.For the algorisms, we first investigated some basic calculation tools, such as matrix multiplications and inversion. Especially for multiplication of larger matrices, several methods, such as column/row wise matrix splitting and Fox algorithm with checker board arrangements are investigated regarding the memory and CPU resource configuration. Furthermore, programs for the bootstrapping and tree based methods were reviewed and their plausibility for the parallel processing, granularity issues and coherency for the task management methods were investigated.Based on these considerations, we installed a distributed parallel processing simulation platform within the heterogeneous computer resources and confirmed its efficiency for use of computer intensive analysis for real data analysis and their performance evaluation, via analyses of multinomial categorical data with over-dispersion problem.

我们采用了Beowulf的一种实现形式。型PC机群的分布式并行计算环境，并研究了这种实现方式的实际实现和使用的优缺点。为了安装该系统，我们对实验室的网络系统和计算机资源进行了重组，利用PVM和MPI建立了分布式并行计算环境（这里我们使用“lam”作为实际实现）。我们开发了一种任务管理方法“时间预测模型”，该方法以管理者-工作者模型的形式控制任务的期望运行时间，并与工作池方法进行了比较，以控制异构计算资源中的任务。在此基础上，我们还考虑了一个分布式并行环境下的集成应用开发环境的基本设计，该环境将支持计算密集型的统计分析，如Bootstrap和基于模拟的分析。在算法方面，我们首先研究了一些基本的计算工具，如矩阵乘法和求逆。特别是对于较大矩阵的乘法，从内存和CPU资源配置的角度，研究了矩阵列/行分裂和棋盘排列的Fox算法等几种方法。在此基础上，建立了一个基于异构计算机资源的分布式并行处理仿真平台，并验证了该平台在真实的数据分析和性能评估中的有效性。通过对具有过离差问题的多项分类数据的分析。