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工作站机群
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  cluster of workstations
    The cluster of workstations is utilized to realize the distributed computation.
    利用工作站机群实现了互联电网安全稳定预防控制和紧急控制一体化在线预决策系统的分布式计算框架。
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    In this paper we introduce a general pvm parallel programme performance visualization software tool kit VP4. On the basis of the characteristics of cluster of workstations, in the design of VP4 we adopt a multistage and incident based performance data collection method.
    本文介绍了一个通用的pvm并行程序性能可视化软件工具VP~4。 针对工作站机群的特点,它采用多层次性能数据采集方法和基于事件的采取策略,这样可以在尽量减少“侵入影响”的前提下,采集并汇总全部性能数据。
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  cluster of workstations
Finally, DPAF is evaluated in theory and tested on a 32-CPU cluster of workstations.
      
The parallel program is developed on a cluster of workstations.
      
The simulator ParSpin was implemented on a heterogeneous, interconnected cluster of workstations based on existing message passing libraries.
      
This limitation may be alleviated by parallel computing using a multiprocessor computer or a cluster of workstations.
      
Discrete Element Modelling on a Cluster of Workstations
      
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  cluster of workstation
Distributed systems based on cluster of workstation are more and more difficult to manage due to the increasing number of processors involved, and the complexity of associated applications.
      
Thus, we feel that such an investigation would be helpful in understanding the strengths and weaknesses of an Ethernet cluster of workstation in the execution of parallel applications.
      
Each cluster of workstation is thus called a Computational Coop Member or simply a Co-op Member.
      
In this way, we can build a Meta-Cluster, or cluster of workstation clusters.
      
With ParaStation coscheduling multithreaded applications run efficiently on a cluster of workstation with user-space communication protocols.
      


In this paper we introduce a general pvm parallel programme performance visualization software tool kit VP4. On the basis of the characteristics of cluster of workstations, in the design of VP4 we adopt a multistage and incident based performance data collection method. Thus it is able to collect and gather all the performance data,provided that the "interference affection"is reduced to the least extent.Then,this software tool kit visulizes the gathered data applicably by using picture and animation. Finally,...

In this paper we introduce a general pvm parallel programme performance visualization software tool kit VP4. On the basis of the characteristics of cluster of workstations, in the design of VP4 we adopt a multistage and incident based performance data collection method. Thus it is able to collect and gather all the performance data,provided that the "interference affection"is reduced to the least extent.Then,this software tool kit visulizes the gathered data applicably by using picture and animation. Finally, experiments are carried out to demonstrate that VP4 can be effectively applied in helping the users to detect performance bottle neck and to improve performance in parallel programme.

本文介绍了一个通用的pvm并行程序性能可视化软件工具VP~4。针对工作站机群的特点,它采用多层次性能数据采集方法和基于事件的采取策略,这样可以在尽量减少“侵入影响”的前提下,采集并汇总全部性能数据。VP~4对汇总的性能数据进行处理后,利用图形与动画生成各种易于使用的可视化性能视图。通过实验表明,本软件工具可以有效的帮助用户发现性能瓶颈,辅助用户开发高性能的并行程序。

Radial Basis Function Neural Networks(RBF NN) are frequently used for regression prediction.But kernel matrix computation for high dimensional data source demands heavy computing power.To shorten computing time,the paper designs a parallel algorithm to compute the kernel function matrix of RBF NN and applies it to the prediction of converter re-vanadium modeling.The paper then implements the algorithm on a cluster of computing workstations using MPI.Finally,experiment is done with the practical data to study...

Radial Basis Function Neural Networks(RBF NN) are frequently used for regression prediction.But kernel matrix computation for high dimensional data source demands heavy computing power.To shorten computing time,the paper designs a parallel algorithm to compute the kernel function matrix of RBF NN and applies it to the prediction of converter re-vanadium modeling.The paper then implements the algorithm on a cluster of computing workstations using MPI.Finally,experiment is done with the practical data to study the speedups and accuracy of the algorithm.

径向基神经网络经常用于回归预测,但是高维的核函数矩阵运算需要花费巨大计算资源.为了缩短计算时间,设计了并行算法用于计算径向基网络核函数矩阵,并将它用于转炉提钒预测模型,在以MPI构建的工作站机群上执行该算法,利用实际数据验证了该算法的加速性和准确性.

In converter re-vanadium there exist a lot of diversity and non-linear factors.From the point of view of statistics and reaction mechanism,it is difficult to build up optimized control model between operating parameters and product goals.A soft sensor and control model for forecast vanadium output by RBF NN is presented to optimize operating parameters of converter. Radial basis function neural network(RBF NN) is frequently used for non-linear regression prediction and control,but the kernel matrix computation...

In converter re-vanadium there exist a lot of diversity and non-linear factors.From the point of view of statistics and reaction mechanism,it is difficult to build up optimized control model between operating parameters and product goals.A soft sensor and control model for forecast vanadium output by RBF NN is presented to optimize operating parameters of converter. Radial basis function neural network(RBF NN) is frequently used for non-linear regression prediction and control,but the kernel matrix computation for high dimensional data source requires heavy computing power.To shorten the computing time,a parallel algorithm to compute the kernel function matrix of RBF NN was designed and applied to the soft sensor and control model of converter re-vanadium.The algorithm was implemented on a cluster of computing workstations using MPI.Finally,the experimental data prove that the algorithm can speed up the computation and its result is accurate.

转炉提钒过程中存在大量多元非线性因素,难以从统计学和机理上建立各操作参数与生产目标的优化控制模型,为优化转炉的操作参数,建立了基于径向基神经网络的半钢钒含量软测量和控制模型。径向基神经网络常用于非线性回归预测和控制,但是高维的核函数矩阵运算需要花费巨大计算资源。为了缩短计算时间,本文设计了并行算法用于计算径向基网络核函数矩阵,并将它用于转炉提钒软测量和控制模型,在以MPI构建的工作站机群上执行该算法,利用实际数据验证了该算法的加速性和准确性。

 
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