# MATLAB DCS

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(→ParFor Parallel Example) |
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=== ParFor Parallel Example === | === ParFor Parallel Example === | ||

− | This example will utilize the parfor parallel loop as [http://www.mathworks.com/ | + | This example will utilize the parfor parallel loop as [http://www.mathworks.com/products/parallel-computing/parallel/accelerate.html defined here]. |

## Revision as of 17:28, 10 March 2010

## Contents |

## Steps to run Matlab

### Simple Matlab Test

A simple test to verify that the Matlab client on Cheaha can check out a license from your server.

Set up your environment with the command:

$ module load mathworks/matlab

As a test, you can run MatLab and access your license server with

$ matlab -c port@license-server -nodesktop -nojvm -r "rand, exit"

For example:

$ module load mathworks/matlab $ matlab -c 27000@licserver.uab.edu -nodesktop -nojvm -r "rand, exit" < M A T L A B (R) > Copyright 1984-2009 The MathWorks, Inc. Version 7.9.0.529 (R2009b) 64-bit (glnxa64) August 12, 2009 To get started, type one of these: helpwin, helpdesk, or demo. For product information, visit www.mathworks.com. ans = 0.8147

This will start matlab without a graphical display and without Java support. This is good just to verify things work, but do not run any significant computations on the Cheaha head node!

MatLab computational work must be run on the compute nodes by submitting a job submission script to the SGE scheduler

### Serial Matlab

These instructions do NOT use the distributed licenses available on cheaha, only using your own client license, and will be restricted to a single cpu.

See the next section for an example using the distributed computing license.

You can create a single cpu Matlab job to run your non parallel computing toolbox code as follows.

Create a job script "matlabtest.qsub" making sure to change:

* YOUR_EMAIL_ADDRESS * h_rt and s_rt to appropriate hard and soft runtime limits * mem_free to the maximum amount of memory that your job will require

#!/bin/bash #$ -S /bin/bash #$ -cwd # #$ -N testMatLab #$ -l h_rt=00:10:00,s_rt=00:08:00,mem_free=2G #$ -j y # #$ -M YOUR_EMAIL_ADDRESS #$ -m eas # module load mathworks/matlab #$ -V matlab -c port@license-server -nodisplay -nojvm < matlab-script

Then submit the script to the scheduler with

$ qsub matlabtest.qsub

Check on it with qstat.

$ qstat -u $USER

### Distributed Matlab

These instructions provide an example of how to create and submit a distributed Matlab job on cheaha. Distributed Matlab jobs require two separate licenses:

* Your own client license that includes the Parallel Computing Toolbox * The Cheaha 128 node Distributed Computing license

The client license will only be needed for as long as it takes Matlab to start the job on the compute nodes (unless you keep the client open, for example using "waitForState(job)" in your Matlab script).

The instructions are a work in progress, so please contact Research Computing support with any questions or corrections.

First, create the working directory for the job

$ mkdir -p ~/jobs/matlab/distrib01/output $ cd ~/jobs/matlab/distrib01

Next, create a simple 2 task distributed Matlab script called "distrib.m" make sure to change:

* email to your email address * time_limit to an appropriate soft runtime limit * hard_time_limit to the maximum wall time for your job * mem_free to the maximum memory needed for each task * remote and local DataLocation to point to your working directory

Don't make any changes to the section labeled "Configure the scheduler"

% Always set these variables email = 'YOUR_EMAIL_ADDRESS'; time_limit = '00:05:00'; hard_time_limit = '00:07:00'; mem_free = '1G'; clusterHost = 'cheaha.uabgrid.uab.edu'; remoteDataLocation = '/home/USERNAME/jobs/matlab/distrib01'; localDataLocation = '/home/USERNAME/jobs/matlab/distrib01/output'; % Configure the scheduler sched = findResource('scheduler', 'type', 'generic'); set(sched, 'DataLocation' , localDataLocation); set(sched, 'ClusterMatlabRoot', '/share/apps/mathworks/matlab'); set(sched, 'HasSharedFilesystem', true ); set(sched, 'ClusterOsType' , 'unix' ); set(sched, 'SubmitFcn', {@sgeSubmitFcn, remoteDataLocation, hard_time_limit, time_limit, mem_free, email}); set(sched, 'DestroyJobFcn', {@sgeDestroyJob}); set(sched, 'GetJobStateFcn', {@sgeGetJobState}); get(sched) job = createJob(sched); % start of user specific commands createTask(job, @rand, 1, {3,3}); createTask(job, @rand, 2, {3,3}); submit(job)

Running the Matlab script will submit 2 SGE jobs, one for each task. The Parallel Computing Toolbox requires Java VM, so notice that for this job we do not include the "-nojvm" switch!

$ module load mathworks/matlab $ matlab -c port@license-server -nodisplay < distrib.m

Check qstat to see that the scheduler now has 2 jobs running, one for each task

$ qstat -u $USER job-ID prior name user state submit/start at queue slots ja-task-ID ----------------------------------------------------------------------------------------------------------------- 110839 0.50167 Job1.1 jdoe r 03/10/2010 16:32:37 all.q@compute-0-12.local 1 110840 0.50083 Job1.2 jdoe r 03/10/2010 16:32:37 all.q@compute-0-12.local 1

The job output can be found in the "output" directory

### Parallel Matlab

These instructions provide an example of how to create and submit a parallel Matlab job on cheaha. Parallel Matlab jobs require two separate licenses:

* Your own client license that includes the Parallel Computing Toolbox * The Cheaha 128 node Distributed Computing license

The client license will only be needed for as long as it takes Matlab to start the job on the compute nodes (unless you keep the client open, for example using "waitForState(job)" in your Matlab script).

Check out this Matlab Help Page for a quick overview of using parallel code in your Matlab scripts.

First, create the working directory for the job

$ mkdir -p ~/jobs/matlab/paralle01/output $ cd ~/jobs/matlab/parallel01

Next, create a simple 4 slot parallel Matlab script called "parjob.m" make sure to change:

* email to your email address * time_limit to an appropriate soft runtime limit * hard_time_limit to the maximum wall time for your job * mem_free to the maximum memory needed for each task * remote and local DataLocation to point to your working directory

Don't make any changes to the section labeled "Configure the scheduler"

% Always set these variables email = 'YOUR_EMAIL_ADDRESS'; time_limit = '00:05:00'; hard_time_limit = '00:07:00'; mem_free = '1G'; clusterHost = 'cheaha.uabgrid.uab.edu'; remoteDataLocation = '/home/USERNAME/jobs/matlab/parallel01'; localDataLocation = '/home/USERNAME/jobs/matlab/parallel01/output'; % Configure the scheduler sched = findResource('scheduler', 'type', 'generic'); set(sched, 'DataLocation' , localDataLocation); set(sched, 'ClusterMatlabRoot', '/share/apps/mathworks/matlab'); set(sched, 'HasSharedFilesystem', true ); set(sched, 'ClusterOsType' , 'unix' ); set(sched, 'ParallelSubmitFcn', {@sgeParallelSubmitFcn, remoteDataLocation, hard_time_limit, time_limit, mem_free, email}); set(sched, 'DestroyJobFcn', {@sgeDestroyJob}); set(sched, 'GetJobStateFcn', {@sgeGetJobState}); get(sched) pjob = createParallelJob(sched); % start of user specific commands createTask(pjob, 'rand', 1, {4}); set(pjob, 'MinimumNumberOfWorkers', 4); set(pjob, 'MaximumNumberOfWorkers', 4); submit(pjob) waitForState(pjob) results = getAllOutputArguments(pjob) celldisp(results)

Running the Matlab script will submit 1 SGE job requesting 4 slots (cpu cores). The Parallel Computing Toolbox requires Java VM, so notice that for this job we do not include the "-nojvm" switch!

$ module load mathworks/matlab $ matlab -c port@license-server -nodisplay < parjob.m

Check qstat to see that the scheduler now has 2 jobs running, one for each task

$ qstat -u $USER job-ID prior name user state submit/start at queue slots ja-task-ID ----------------------------------------------------------------------------------------------------------------- 110857 0.00000 Job1 jdoe r 03/10/2010 17:20:08 4

The job output can be found in the "output" directory

### ParFor Parallel Example

This example will utilize the parfor parallel loop as defined here.