Difference between revisions of "Support:Documents:Manual:Distributed Computing with COMKAT"
Line 5: | Line 5: | ||
== How can MDCS help COMKAT == | == How can MDCS help COMKAT == | ||
− | In kinetic modelling, there are several time-consuming computations. For example, it takes several days to perform a pixel-wise estimation for kinetic parameters. One solution to reduce the computational time is to use MDCS. The example can be found in the following [http://comkat.case.edu/comkat/comkat_wiki/index.php?title=Support:Documents:Examples:Estimate_Parametric_Image_with_Matlab_Distributed_Computing_Server link]. One of the other applications is about setting initial conditions for estimating kinetic parameters. Generally, the accuracy of estimated kinetic parameters is easily afftected by their initial conditions. However, there is no standard rule to find an appropriate initial guess. One alternative is to perform parameter estimation with different initial conditions and to calculate the mean value from these different conditions. However, this method is limited by its computational time. | + | In kinetic modelling, there are several time-consuming computations. For example, it takes several days to perform a pixel-wise estimation for kinetic parameters. One solution to reduce the computational time is to use MDCS. The example can be found in the following [http://comkat.case.edu/comkat/comkat_wiki/index.php?title=Support:Documents:Examples:Estimate_Parametric_Image_with_Matlab_Distributed_Computing_Server link]. One of the other applications is about setting initial conditions for estimating kinetic parameters. Generally, the accuracy of estimated kinetic parameters is easily afftected by their initial conditions. However, there is no standard rule to find an appropriate initial guess. One alternative is to perform parameter estimation with different initial conditions and to calculate the mean value from these different conditions. However, this method is limited by its computational time. Fortunately, its computational load can be reduce by using MDCS. The following paragraph will describe the setting of MDCS. |
==Setting Matlab Distributed Computing Server (MDCS)== | ==Setting Matlab Distributed Computing Server (MDCS)== |
Revision as of 18:51, 3 April 2009
What is Matlab Distributed Computing Server (MDCS)
The purpose of using Matlab Distributed Computing Server (MDCS) is to reduce computational time for data-intensive problems. It is executed by MATLAB and Simulink based apllications on a computer cluster, and it is available for all hardware platforms and operating systems. More detail introduction about MDCS can be found in this link.
How can MDCS help COMKAT
In kinetic modelling, there are several time-consuming computations. For example, it takes several days to perform a pixel-wise estimation for kinetic parameters. One solution to reduce the computational time is to use MDCS. The example can be found in the following link. One of the other applications is about setting initial conditions for estimating kinetic parameters. Generally, the accuracy of estimated kinetic parameters is easily afftected by their initial conditions. However, there is no standard rule to find an appropriate initial guess. One alternative is to perform parameter estimation with different initial conditions and to calculate the mean value from these different conditions. However, this method is limited by its computational time. Fortunately, its computational load can be reduce by using MDCS. The following paragraph will describe the setting of MDCS.
Setting Matlab Distributed Computing Server (MDCS)
To start parallel computing, user must install MDCS that described in this document. To get appropriate instructions, user should click the above link and fill out the information about your cluster computer's platforms, Matlab end user's platforms, installation location, scheduler and licensing, and there should be four stages for installing MDCS.
Following the below stages if it is the first time you are building MATLAB Distributed Computing Server
Stage 1: Install MATLAB Distributed Computing Server
Stage 2: Configure Your Cluster for use with MathWorks Job Manager
Stage 3: Install Parallel Computing Toolbox
In brief, stage 1 is to install MDCS on the head node and worker nodes and then start the license manager on the head node. Stage 2 is to renew mdce services on all nodes. Stage 3 is to install parallel computing toolbox if you will write Matlab applications.
Setting Up Client (User's Computer) for Matlab Distributed Computing
Following the below stage if it is the first time you are using MATLAB Distributed Computing
Stage 4: Test Your Parallel Computing Environment
The customized document generated by clicking on the document link above gives details on how to set up the user's computer. Please review that document for details and screen snapshots. In brief, the steps for a Windows client are to click on the Parallel menu item (to the right of File, Edit, ... in the MATLAB window). If this is the first time you are using MATLAB Distributed Computing, click Manage. On the File menu, click New and select the desired jobmanager type. To use MATLAB's built-in manager, select jobmanager. Define name and other properties for the configuration including the Job manager host name (e.g. an IP address) and the job name manager. On the jobs tab, specify the maximum and minimum numbers of workers. Click OK/save.