Difference between revisions of "Matlab Examples using MDCS"

From HPC users
Jump to navigationJump to search
Line 2: Line 2:


== Example application ==
== Example application ==
Consider the Matlab .m-file myExample_2DRandWalk.m (listed below), which among other things
illustrates the use of sliced variables and independent stremas of random numbers for use
with parfor-loops.
     
This example program generates a number of N independent
2D random walks (a single step has steplength 1 and a
random direction). Each random walk performs tMax steps.
At each step t, the radius of gyration (Rgyr) of walk i
is stored in the array Rgyr_t in the entry Rgyr_t(i,t).
While the whole data is availabe for further postprocessing,
only the average radius of gyration Rgyr_av and the respective
standard error Rgyr_sErr for the time steps 1...tMax are
computed immediately and stored in an output file on HERO.




As liste above, the .m-file depends on the following files:
singleRandWalk.m, implementing a single random walk;
averageRgyr.m, which computes the average radius of gyration
of the random walks for time steps 1...tMax; basicStats.m,
implenting routines for the three basic summary statistic average,
corrected variance and standard error.


For test purposes one might execute the myExample_2DRandWalk.m
directly from within a Matlab session on a local Desktop PC.
So as to sumbit the respective job to the local HPC system one
might assemble the following job submission script, called mySubmitScript.m:
  <nowiki>
sched = findResource('scheduler','Configuration','HERO');
jobRW =...
  batch(...
        sched,...   
        'myExample_2DRandWalk',...
        'matlabpool',2,...
        'FileDependencies',{...
                          'singleRandWalk.m',...
                          'averageRgyr.m',...
                          'basicStats.m'...
                          }...
        );
  </nowiki>


== Specifying file dependencies ==
== Specifying file dependencies ==

Revision as of 17:38, 6 June 2013

A few examples for Matlab applications using MDCS (prepared using Matlab version R2011b) are illustrated below.

Example application

Consider the Matlab .m-file myExample_2DRandWalk.m (listed below), which among other things illustrates the use of sliced variables and independent stremas of random numbers for use with parfor-loops.

This example program generates a number of N independent 2D random walks (a single step has steplength 1 and a random direction). Each random walk performs tMax steps. At each step t, the radius of gyration (Rgyr) of walk i is stored in the array Rgyr_t in the entry Rgyr_t(i,t). While the whole data is availabe for further postprocessing, only the average radius of gyration Rgyr_av and the respective standard error Rgyr_sErr for the time steps 1...tMax are computed immediately and stored in an output file on HERO.


As liste above, the .m-file depends on the following files: singleRandWalk.m, implementing a single random walk; averageRgyr.m, which computes the average radius of gyration of the random walks for time steps 1...tMax; basicStats.m, implenting routines for the three basic summary statistic average, corrected variance and standard error.

For test purposes one might execute the myExample_2DRandWalk.m directly from within a Matlab session on a local Desktop PC. So as to sumbit the respective job to the local HPC system one might assemble the following job submission script, called mySubmitScript.m:

 
sched = findResource('scheduler','Configuration','HERO');

jobRW =...
   batch(...
        sched,...    
        'myExample_2DRandWalk',...
        'matlabpool',2,...
        'FileDependencies',{...
                           'singleRandWalk.m',...
                           'averageRgyr.m',...
                           'basicStats.m'...
                           }...
        );
  

Specifying file dependencies

Specifying path dependencies

Storing data on HERO

Further examples