Matlab Examples using MDCS

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

Example application: 2D random walk

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 (below it will also be shown how to store the data in an output file on HERO for further postprocessing).

 
%% FILE:  myExample_2DRandWalk.m
%  BRIEF: illustrate sliced variables and independent streams 
%         of random numbers for use with parfor-loops
%         
%  DEPENDENCIES:
%    singleRandWalk.m   - implements single random walk
%    averageRgyr.m      - computes average radius of gyration
%                         for time steps 1...tMax
%
%  AUTHOR: Oliver Melchert
%  DATE:   2013-06-05
%

N       = 10000;         % number of independent walks
tMax    = 100;           % number of steps in individual walk
Rgyr_t  = zeros(N,tMax); % matrix to hold results: row=radius 
                         % of gyration as fct of time; 
                         % col=independent random walk instances

parfor n=1:N

        % create random number stream seeded by the
        % current value of n; you can obtain a list
        % of all possible random number streams by
        % typing RandStream.list in the command window
        myStream = RandStream('mt19937ar','Seed',n);

        % obtain radius of gyration as fct of time for 
        % different independent random walks (indepence 
        % of RWs is ensured by connsidering different 
        % random number streams for each RW instance)
        Rgyr_t(n,:) = singleRandWalk(myStream,tMax);

end

% compute average Rgyr and its standard error for all steps
[Rgyr_av,Rgyr_sErr] = averageRgyr(Rgyr_t);

As liste above, the .m-file depends on the following files:

singleRandWalk.m, implementing a single random walk, reading:

 
function [Rgyr_t]=singleRandWalk(randStream,tMax)
% Usage:  [Rgyr_t]=singleRandWalk(randStream,tMax)
% Input:
%   randStream - random number stream
%   tMax       - number of steps in random walk
% Output:
%   Rgyr_r     - array holding the radius of gyration
%                for all considered time steps

  x=0.;y=0.; % initial walker position
  Rgyr_t = zeros(tMax,1);
  for t = 1:tMax
    % implement random step
    phi=2.*pi*rand(randStream);
    x = x+cos(phi);
    y = y+sin(phi);
    % record radius of gyration for current time
    Rgyr_t(t)=sqrt(x*x+y*y);
  end
end

averageRgyr.m, which computes the average radius of gyration of the random walks for time steps 1...tMax, reading:

 
function [avList,stdErrList]=averageRgyr(rawDat)
% Usage: [av]=averageRgyr(rawDat)
% Input:
%  rawData   - array of size [N,tMax] where N is the
%              number of independent random walks and
%              tMax is the number of steps taken by an
%              individual walk
% Returns:
%  av         - aveage radius of gyration for the steps

 [Lx,Ly]=size(rawDat);
  avList = zeros(Ly,1);
  stdErrList = zeros(Ly,1);
  for i = 1:Ly
    [av,var,stdErr] = basicStats(rawDat(:,i));
    avList(i) = av;
    stdErrList(i) = stdErr; 
  end
end

function [av,var,stdErr]=basicStats(x)
% usage: [av,var,stdErr]=basicStats(x)
% Input: 
%  x   - list of numbers
% Returns:
%  av  - average 
%  var - variance
%  stdErr - standard error
  av=sum(x)/length(x);
  var=sum((x-av).^2)/(length(x)-1);
  stdErr=sqrt(var/length(x));
end

For test purposes one might execute the myExample_2DRandWalk.m directly from within a Matlab session on a local Desktop PC.

Specifying file dependencies

So as to sumbit the respective job to the local HPC system one might assemble the following job submission script, called mySubmitScript_v1.m:

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

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

In the above job submission script, all dependent files are listed as FileDependencies. I.e., the .m-files specified therein are copied from your local desktop PC to the HPC system at run time.

Now, from within a Matlab session I navigate to the Folder where the above .m-files are located in and call the job submission script, i.e.:

 
>> cd MATLAB/R2011b/example/myExamples_matlab/RandWalk/
>> mySubmitScript_v1
runtime    = 24:0:0 (default)
memory     = 1500M (default)
diskspace  = 50G (default)

before the job is actually submitted, I need to specify my user ID and password, of course. Once the job is successfully submitted, I can check the state of the job via typing jobRW.state. However, if you want to get some more information on the status of your job, you might want to try to log-in on the HPC system and to simply type the command qstat on the commandline. This will yield several details related to your job which you might process further to see on which execution nodes your job runs, why it won't start directly etc. Note that MATLAB provides only a wrapper for the qstat command which in some cases result in a misleading output. E.g., if, for some reason, your job changes to the error-state it might be that MATLAB erroneously reports it to be in the finished-state.

Once the job (really) has finished, i.e.,

 
>> jobRW.state

ans =

finished

I might go on and load the results to my desktop computer, giving

 
>> res=load(jobRW);
>> res

res = 

            N: 10000
      Rgyr_av: [100x1 double]
    Rgyr_sErr: [100x1 double]
       Rgyr_t: [10000x100 double]
          ans: 'finished'
          res: [1x1 struct]
         tMax: 100

However, note that there are several drawbacks related to the usage of FileDependencies: E.g.,

each worker gets an own copy of the respective .m-files when the job starts (in particular, workers that participate in the computing process do not share a set of .m-files in a common location),
the respective .m-files are not available on the HPC system once the job has finished,
comparatively large input files need to be copied to the HPC system over and over again, if several computations on the same set of input data are performed.

In many cases a different procedure, based on specifying PathDependencies, outlined below in detail, might be recommendend.

Specifying path dependencies

Basically there are two ways to specify path dependencies. You might either specify them in your job submission script or directly in your main MATLAB .m-file. Below, both approaches are illustrated.

Modifying the job submission script

The idea underlying the specification of path dependencies is that there might be MATLAB modules (or sets of data) you want to routinely use over and over again. Then, having these modules available on your local desktop computer and using file dependencies to copy them to the HPC system at run time results in time and memory consuming, unnecessary operations.

As a remedy you might adopt the following two-step procedure:

copy the respective modules to the HPC system
upon submitting the job from your local desktop pc, indicate by means of the key word pathDependencies where (on HERO) the respective data files can be found.

This eliminates the need to copy the respective files using the fileDependencies statement. An example of how to accomplish this for the random walk example above is given below.

Just for arguments, say, the content of the two module files singleRandWalk.m and averageRgyr.m will not be changed in the near future and you want to use both files on a regular basis when you submit jobs to the HPC cluster. Hence, it would make sense to copy them to the HPC system and to specify within your job submission script where they can be found for use by any execution host. Following the above two steps you might proceed as follows:

1. create a folder where you will copy both files to. To facilitate intuition and to make this as explicit as possible, I created the folder

 /user/fk5/ifp/agcompphys/alxo9476/SIM/MATLAB/R2011b/my_modules/random_walk

and copied both files there, see

 
alxo9476@hero01:~/SIM/MATLAB/R2011b/my_modules/random_walk$ ls
averageRgyr.m  singleRandWalk.m

2. Now, it is no more necessary to specify both files as file dependencies as in the example above. Instead, in your job submission file (here called mySumbitScript_v2.m) you might now specify a path depenency (which relates to your filesystem on the HPC system) as follows:

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

jobRW =...
   batch(...
        sched,...    
        'myExample_2DRandWalk',...
        'matlabpool',2,...
        'PathDependencies',{'/user/fk5/ifp/agcompphys/alxo9476/SIM/MATLAB/R2011b/my_modules/random_walk'}...
        );

This has the benefit that the files will not be copied to the HPC system at run time and that there is only a single copy of those files on the HPC system, which can be used by all execution hosts (so no multiple copies of the same files necessarry as with the use of file dependencies).

Again, from within a Matlab session navigate to the folder where the job submission file and the main file myExample_2DRandWalk.m are located in and call the job submission script. For me, this reads:

 
>> cd MATLAB/R2011b/example/myExamples_matlab/RandWalk/
>> mySubmitScript_v2
runtime    = 24:0:0 (default)
memory     = 1500M (default)
diskspace  = 50G (default)

again, before the job is actually submitted, I need to specify my user ID and password (since I started a new MATLAB session in between). Once the job has finished I might go on and load the results to my desktop computer, giving

 
>> res=load(jobRW);
>> res

res = 

            N: 10000
      Rgyr_av: [100x1 double]
    Rgyr_sErr: [100x1 double]
       Rgyr_t: [10000x100 double]
          ans: 'finished'
          res: [1x1 struct]
         tMax: 100

Modifying the main m-file

As an alternative to the above procedure, you might add the folder

 /user/fk5/ifp/agcompphys/alxo9476/SIM/MATLAB/R2011b/my_modules/

to your local MATLAB path by adding the single line

 addpath(genpath('/user/fk5/ifp/agcompphys/alxo9476/SIM/MATLAB/R2011b/my_modules/'));

to the very beginning of the file myExample_2DRandWalk.m. For completeness, note that this adds the above folder my_modules and all its subfolders to your MATLAB path. Consequently, all the m-files contained therein will be available to the execution nodes which contribute to a MATLAB session.

In this case, a proper job submission script (mySubmitScript_v3.m) reads simply:

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

jobRW =...
   batch(...
        sched,...    
        'myExample_2DRandWalk',...
        'matlabpool',2...
        );

As a personal comment, note that, from a point of view of submitting a job to the HPC System, I would always prefer the explicit way of stating path dependencies in the job submission file over the implicit way of indirectly implying them using a modification of the main m-file. The latter choice seems much more vulnerable to later changes!

Storing data on HERO

Consider a situation where your application produces lots of data you want to store for further postprocessing. Often, in particular when you produce lots of data, you also don't want the data to be copied back to your desktop computer, immediately. Instead you might want to store the data on HERO and perhaps mount your HPC-homedirectory later to relocate the data (or the like). Note that, as a default HERO user, you have 110GB of disk space available under your home directory. Below it is illustrated, by means of the random walk example introduced earlier, how to store output data on the HPC system.

The only thing you need to do is to specify a path within your main m-file under which the data should be stored. Therefore you first have to create the corresponding sequence of folders if they do not exist already. To facilitate intuition: in my case I decided to store the data under the path

 /user/fk5/ifp/agcompphys/alxo9476/SIM/MATLAB/R2011b/my_data/random_walk/

I created the folder my_data/rand_walk/ for that purpose. Right now, the folder is empty.

In the main file (here called myExample_2DRandWalk_saveData.m) I implemented the following changes:

 
N       = 10000;         % number of independent walks
tMax    = 100;           % number of steps in individual walk
Rgyr_t  = zeros(N,tMax); % matrix to hold results: row=radius 
                         % of gyration as fct of time; 
                         % col=independent random walk instances

% absolute path to file on HERO where data will be saved 
outFileName=sprintf('/user/fk5/ifp/agcompphys/alxo9476/SIM/MATLAB/R2011b/my_data/random_walk/rw2d_N%d_t%d.dat',N,tMax);

parfor n=1:N

        % create random number stream seeded by the
        % current value of n; you can obtain a list
        % of all possible random number streams by
        % typing RandStream.list in the command window
        myStream = RandStream('mt19937ar','Seed',n);

        % obtain radius of gyration as fct of time for 
        % different independent random walks (indepence 
        % of RWs is ensured by connsidering different 
        % random number streams for each RW instance)
        Rgyr_t(n,:) = singleRandWalk(myStream,tMax);

end

% compute average Rgyr and its standard error for all steps
[Rgyr_av,Rgyr_sErr] = averageRgyr(Rgyr_t);

% write data to output file on HERO
saveData_Rgyr(outFileName,Rgyr_av, Rgyr_sErr);

Note that the outFileName is specified directly in the main m-file (there are also more elegant ways to accomplish this, however, for the moment this will do!) and the new function, termed saveData_Rgyr is called. The latter function will just write out some statistical summary measures related to the gyration radii of the 2D random walks. For completeness, it reads:

 
function saveData(fileName,Rgyr_av,Rgyr_sErr)
% Usage: saveData(fileName,myData)
% Input:
%  fileName - name of output file
%  myData   - data to be saved in file
% Returns:      nothing
  outFile  = fopen(fileName,'w');
  fprintf(outFile,'# t Rgyr_av Rgyr_sErr \n');
  for i = 1:length(Rgyr_av)
    fprintf(outFile,'%d %f %f\n',i,Rgyr_av(i),Rgyr_sErr(i));
  end
  fclose(outFile);
end

Now, say, my opinion on the proper output format is not settled yet and I consider to experiment with different kinds of output formatting styles, for that matter. Then it is completely fine to specify some of the dependent files as path dependencies (namely those that are unlikely to change soon) and others as file dependencies (namely those which are under development). Here, joining in the specification of path dependencies within a job submission script, a proper submission script (here called mySubmitScript_v4.m) might read:

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

jobRW =...
   batch(...
        sched,...    
        'myExample_2DRandWalk_saveData',...
        'matlabpool',2,...
        'FileDependencies',{'saveData_Rgyr.m'},...
        'PathDependencies',{'/user/fk5/ifp/agcompphys/alxo9476/SIM/MATLAB/R2011b/my_modules/random_walk'}...
        );

Again, starting a MATLAB session, changing to the directory where the myExample_2DRandWalk_saveData.m and saveData_Rgyr.m files are located in, launching the submission script mySubmitScript_v5.m and waiting for the job to finish, i.e.

 
>> cd MATLAB/R2011b/example/myExamples_matlab/RandWalk/
>> mySubmitScript_v4
runtime    = 24:0:0 (default)
memory     = 1500M (default)
diskspace  = 50G (default)
>> jobRW.state

ans =

finished

Once the job is done, the output file rw2d_N10000_t100.dat (as specified in the main script myExample_2DRandWalk_saveData.m) is created in the folder under the path

 /user/fk5/ifp/agcompphys/alxo9476/SIM/MATLAB/R2011b/my_data/random_walk/

which contains the output data in the format as implemented by the function saveData_Rgyr.m, i.e.

 
alxo9476@hero01:~/SIM/MATLAB/R2011b/my_data/random_walk$ head rw2d_N10000_t100.dat 
# t Rgyr_av Rgyr_sErr 
1 1.000000 0.000000
2 1.273834 0.006120
3 1.572438 0.007235
4 1.795441 0.008645
5 1.998900 0.009714
6 2.168326 0.010867
7 2.355482 0.011766
8 2.519637 0.012721
9 2.683048 0.013456

Again, after the job has finished, the respective file remains on HERO and is available for further postprocessing. It is not automatically copied to your local desktop computer.

Postprocessing of the simulated data

Just for completeness a brief analysis and display of the 2D random walk data is presenter here. Once the job has finished and the resulting data is loaded to the desktop computer via res=load(jobRW); its possible to postprocess and display some of the results via the m-file finalPlot.m which reads:

 
function finalPlot(res)
% Usage: finalPlot(res) 
% Input:
%  res   - simulation results for 2D rand walks 
% Returns: (none)
%
  subplot(1,2,1), plotHelper(res);
  subplot(1,2,2), histHelper(res);
end

function plotHelper(res)
% Usage: plotHelper(res) 
%
% implements plot of the gyration radius as
% function of the walk length 
%
% Input:
%  res   - simulation results for 2D rand walks 
% Returns: (none)
%
  xRange=1:1:100;
  loglog(xRange,res.Rgyr_av,'--ko');
  title('Radius of gyration for 2D random walks');
  xlabel('t');
  ylabel('Rgyr(t)');
end

function histHelper(res)
% Usage: histHelper(res) 
%
% implements histogram of the pdf for gyration 
% radii at chosen times steps 
% of the walk length 
%
% Input:
%  res   - simulation results for 2D rand walks 
% Returns: (none)
%
  set(0,'DefaultAxesColorOrder',[0 0 0],...
        'DefaultAxesLineStyleOrder','-+|-o|-*');
  tList=[10,50,100];
  [events,key]=hist(res.Rgyr_t(:,tList),20);
  semilogy(key,events);
  title('Approximate pdf for radius of gyration for 2D random walks');
  xlabel('Rgyr');
  ylabel('pdf(Rgyr)');
  key = legend('t= 10','t= 50','t=100');
end

Therein, the main function calls two subfunctions:

plotHelper(res): implements the gyration radius of the 2D random walks as a function of the number of steps in the walk (i.e. the elapsed time since the walk started). The radius of gyration of such an unhindered 2D random walks generally scales $R_{{\rm {gyr}},t}\propto t^{2}$ (for not too short times $t$ ).
histHelper(res): implements histograms of the probability density function (pdf) for the gyration radii at specified time steps. In the above example the time steps $t=10,~50$ and $100$ where chosen. For not too short times, one can expect that the pdf is properly characterized by the Rayleigh distribution $p(R_{{\rm {gyr}},t})={\frac {R_{{\rm {gyr}},t}}{\sigma }}\exp {-R_{{\rm {gyr}},t}^{2}/(2\sigma ^{2})}$ .

Calling the m-file should yield a figure similar to the following:

Recovering jobs (after closing and restarting MATLAB)

Once you submitted one or several jobs, you might just shut down your local MATLAB session. If you open up a new MATLAB session later on, you can recover the jobs you sent earlier by first getting a connection to the scheduler via

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

You can go on and list the jobs in the database using

 sched.Jobs

Further, to get a grip on the very first job in the list you might type

 myJob_no1 = sched.Jobs(1)

which recovers the respective job and lists some of the job details already.

Monitoring Jobs on the HPC system

Once you submitted your job to the scheduler, in our case the third-party scheduler SGE, you have several options to check the status of your job. Of course, if the job you submitted is called myJob, you could just type

 myJob.state

on your MATLAB command window. However, if you want to get some more information on the status of your job, you might want to try to log-in on the HPC system and to simply type the command qstat on the commandline. This will yield several details related to your job which you might process further to see on which execution nodes your job runs, why it won't start directly etc. Note that MATLAB provides only a wrapper for the qstat command which in some cases result in a misleading output. E.g., if, for some reason, your job changes to the error-state it might be that MATLAB erroneously reports it to be in the finished-state. In this regard, loging in on the HPC system might shed some more light on how the state of your job actually is. Consider the following example:

To make this as explicit as possible, I will document here an exemplary job I submitted to the HPC system. In particular, I submitted the above random Walk example using the mySubmitScript_v2.m job-submission script. I submitted the job from my local desktop PC (i.e. the Client), of course, and specified an overall amount of 3 workers to work on the job. As soon as the job is sent to the HPC system, it is handed over to the Scheduler (here, as third-party scheduler we use submit the job via SGE), which assigns a unique job-Id to the job and places the job in a proper queue. Now, logged in to my HERO account and immediately after having submitted the job, I type

 qstat

on the commandline and find

 
job-ID  prior   name       user         state submit/start at     queue                  slots ja-task-ID 
---------------------------------------------------------------------------------------------------------
 760391 0.00000 Job123     alxo9476     qw    07/08/2013 14:27:26                            3

According to this, the job with job-ID 760391 has priority 0.00000, name Job123 and resides in state qw loosely translated to "enqueued and waiting". Also, the above output indicates that the job requires a number of 3 slots, which, in MATLAB terms can be translated to 3 workers. The column for the ja-task-ID, referring to the id of the particular task stemming from the execution of a job array (we don't work through a job array since we submitted a single job), is actually empty and of no importance, here. Soon after, the priority of the job will take a value in between 0.5 and 1.0 (usually only slightly above 0.5), slightly increasing until the job starts. Here, after waiting a few seconds qstat triggers the output

 
job-ID  prior   name       user         state submit/start at     queue                  slots ja-task-ID 
---------------------------------------------------------------------------------------------------------
 760391 0.50535 Job123     alxo9476     r     07/08/2013 14:28:03 mpc_std_shrt.q@mpcs021     3

indicating that the job is now in state r, i.e. running, and that the so called Master process (which coordinates the 3 workers) is started on the execution host mpcs021. To see where the individual workers are started just type

 qstat -g t

to get a more detailed view on the job:

 
job-ID  prior   name       user         state submit/start at     queue                  master ja-task-ID 
----------------------------------------------------------------------------------------------------------
 760391 0.50535 Job123     alxo9476     r     07/08/2013 14:28:03 mpc_std_shrt.q@mpcs003 SLAVE         
                                                                  mpc_std_shrt.q@mpcs003 SLAVE         
 760391 0.50535 Job123     alxo9476     r     07/08/2013 14:28:03 mpc_std_shrt.q@mpcs021 MASTER        
                                                                  mpc_std_shrt.q@mpcs021 SLAVE

So, apparently, the job was split across the two execution hosts mpcs021 and mpcs003. Even more information on the job is available if you use qstat with the particular job-Id, i.e. from the query

 qstat -j 760391

one obtains a lot of information related to job 760391, here I show just the first few lines related to the job above

 
==============================================================
job_number:                 760391
exec_file:                  job_scripts/760391
submission_time:            Mon Jul  8 14:27:26 2013
owner:                      alxo9476
uid:                        59118
group:                      ifp
gid:                        12400
sge_o_home:                 /user/fk5/ifp/agcompphys/alxo9476
sge_o_log_name:             alxo9476
sge_o_path:                 /cm/shared/apps/gcc/4.7.1/bin:/cm/shared/apps/system/bin:/usr/lib64/qt-3.3/bin:/usr/kerberos/bin:/usr/local/bin:/bin:/usr/bin:/sbin:/usr/sbin:/sbin:/usr/sbin:/cm/shared/apps/sge/6.2u5p2/bin/lx26-amd64:/cm/shared/apps/sge/6.2u5p2/local/bin
sge_o_shell:                /bin/bash
sge_o_workdir:              /user/fk5/ifp/agcompphys/alxo9476
sge_o_host:                 hero01
account:                    sge
merge:                      y
hard resource_list:         h_rt=86400,h_vmem=1500M,h_fsize=50G
mail_list:                  alxo9476@hero01.cm.cluster
notify:                     FALSE
job_name:                   Job123
stdout_path_list:           NONE:NONE:/user/fk5/ifp/agcompphys/alxo9476/SIM/MATLAB/R2011b/jobData/Job123/Job123.mpiexec.log
jobshare:                   0
shell_list:                 NONE:/bin/sh
env_list:                   MDCE_DECODE_FUNCTION=parallel.cluster.generic.parallelDecodeFcn,MDCE_STORAGE_CONSTRUCTOR=makeFileStorageObject,MDCE_JOB_LOCATION=Job123,MDCE_MATLAB_EXE=/cm/shared/apps/matlab/r2011b/bin/worker,MDCE_MATLAB_ARGS=-parallel,MDCE_DEBUG=true,MDCE_STORAGE_LOCATION=/user/fk5/ifp/agcompphys/alxo9476/SIM/MATLAB/R2011b/jobData,MDCE_CMR=/cm/shared/apps/matlab/r2011b,MDCE_TOTAL_TASKS=3
script_file:                /user/fk5/ifp/agcompphys/alxo9476/SIM/MATLAB/R2011b/jobData/Job123/parallelJobWrapper.sh
parallel environment:  mdcs range: 3
...

the rest of the output refers to not so easy to interpret scheduling info that basically summarizes which execution hosts where not considered. From this you can get all kinds of information about the job, e.g. a summary of specified resources (see hard resource_list) and the standard output path to which all output regarding the startup of the workers and the job are send to (see stdout_path_list). As further detail, note that the parallel environment used to process the job is called mdcs, which currently has an overall number of 224 slots (i.e. workers) available.

As soon as the job is finished (either successfully or it failed with some kind of error), it is not possible to retrieve information using qstat. However, for finished jobs you can use the command qacct to obtain some information about the finished jobs. Here, for the above job I type

 qacct -j 760391

and obtain

 
==============================================================
qname        mpc_std_shrt.q      
hostname     mpcs021.mpinet.cluster
group        ifp                 
owner        alxo9476            
project      NONE                
department   defaultdepartment   
jobname      Job123              
jobnumber    760391              
taskid       undefined
account      sge                 
priority     0                   
qsub_time    Mon Jul  8 14:27:26 2013
start_time   Mon Jul  8 14:28:05 2013
end_time     Mon Jul  8 14:30:12 2013
granted_pe   mdcs                
slots        3                   
failed       0    
exit_status  0                   
ru_wallclock 127          
ru_utime     18.922       
ru_stime     3.373        
ru_maxrss    384992              
ru_ixrss     0                   
ru_ismrss    0                   
ru_idrss     0                   
ru_isrss     0                   
ru_minflt    232574              
ru_majflt    3142                
ru_nswap     0                   
ru_inblock   0                   
ru_oublock   0                   
ru_msgsnd    0                   
ru_msgrcv    0                   
ru_nsignals  0                   
ru_nvcsw     53953               
ru_nivcsw    9178                
cpu          22.296       
mem          0.002             
io           0.001             
iow          0.000             
maxvmem      356.406M
arid         undefined

Among the details listed there might be a few that might help in the process of interpreting what might have happened to your job in case something went wrong. E.g. see the keywords failed and exit_status. Also the maximally consumed memory maxvmem might be of interest in the process of finding errors.

Matlab Examples using MDCS

Contents

Example application: 2D random walk

Specifying file dependencies

Specifying path dependencies

Modifying the job submission script

Modifying the main m-file

Storing data on HERO

Postprocessing of the simulated data

Recovering jobs (after closing and restarting MATLAB)

Monitoring Jobs on the HPC system

Other Topics

Permision denied upon attempted passphraseless login on remote host

Further troubleshooting

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