Matlab on Pegasus¶
There are several ways to run MATLAB commands/jobs interactively, with or without the graphical interface.
Graphical Interface Mode¶
To run MATLAB using graphical interface mode, connect with display forwarding. For more information about display forwarding, see Forwarding the Display.
Load and launch matlab on one of the interactive compute nodes as shown
below. If you belong to more than one project, specify the
[username@pegasus ~]$ module load matlab [username@pegasus ~]$ bsub -Is -q interactive -XF -P projectID matlab
Once the interactive MATLAB graphical desktop is loaded, you can then run MATLAB commands or scripts in the MATLAB command window. The results will be shown in the MATLAB command window and the figure/plot will be displayed in new graphical windows on your computer. See examples below.
>> x = rand(1,100); >> plot(x); >> >> x = [0: pi/10: pi]; >> y = sin(x); >> z = cos(x); >> figure; >> plot(x, y); >> hold('on'); >> plot(x, z, '--');
Graphical Interactive Mode with no graphical desktop window¶
Running MATLAB in a full graphical mode may get slow depending on the
network load. Running it with
-nodesktop option will use your
current terminal window (in Linux/Unix) as a desktop, while allowing you
still to use graphics for figures and editor.
[username@pegasus ~]$ module load matlab [username@pegasus ~]$ bsub -Is -q interactive -XF -P projectID matlab -nodesktop
Non-Graphical interactive Mode¶
If your MATLAB commands/jobs do not need to show graphics such as
figures and plots, or to use a built-in script editor, run the MATLAB in
the non-graphical interactive mode with
Open a regular ssh connection to Pegasus.
[username@pegasus ~]$ module load matlab [username@pegasus ~]$ bsub -Is -q interactive -P projectID matlab -nodisplay
This will bring up the MALAB command window:
< M A T L A B (R) > Copyright 1984-2013 The MathWorks, Inc. R2013a (188.8.131.524) 64-bit (glnxa64) February 15, 2013 No window system found. Java option 'Desktop' ignored. To get started, type one of these: helpwin, helpdesk, or demo. For product information, visit www.mathworks.com. >>
To exit, type
quit. Again, remember to import the
prepared LSF configuration file mentioned above if you want to use
MATLAB parallel computing.
For off-line non-interactive computations, submit the MATLAB script to
the LSF scheduler using the
bsub command. For more information about
job scheduling, see Scheduling Jobs. Example
single-processor job submission:
[username@pegasus ~]@ bsub < example.job
#BSUB -J example #BSUB -q general #BSUB -P projectID #BSUB -n 1 #BSUB -o example.o%J #BSUB -e example.e%J matlab -nodisplay -r my_script
In this example, “my_script” corresponds to “my_script.m” in the current working directory.
After the job is finished, the results will be saved in the output file
named “example.o######” where “######” is a
assigned by LSF when you submit your job.
MATLAB has software products to enable parallel computations for multi-core computers as well as for multiple-node computer clusters. The latter case scenario requires a job scheduler, such as LSF on Pegasus cluster.
The MATLAB product for the parallel processing that uses the cores of the same node is the “Distributed Computing Toolbox/DCT” (also appears in MATLAB documentation under the name of “Parallel Computing Toolbox”). Licensed MATLAB software product for a computer cluster is called “Distributed Computing Engine/DCE” (also appears in documentation as “MATLAB Distributed Computing Server”).
Single-node parallel MATLAB jobs (up to 16 cpus)¶
For a single-node parallel job, MATLAB Distributed Computing Toolbox (licensed software) is used. It has a build-in default MATLAB cluster profile ‘local’, from which the pool of MatlabWorkers can be reserved for computations. The default number of MatlabWorkers is 12. You can specify up to 15 on a single Pegasus node using the general queue, and 16 cpus using the parallel queue. For more information about queue and parallel resource distribution requirements, see Scheduling Jobs.
Refer to MATLAB documentation on the ways to adapt your script for
multi-processor calculations. One of the parallel tools in MATLAB is the
parfor loop replacing the regular
for loop, and in the example
is given below:
%============================================================== % dct_example.m % Distributed Computing Toolbox (DCT) % Example: Print datestamp within a parallel "parfor" loop %============================================================== %% Create a parallel pool of MatlabWorkers on the current working node: parpool('local',16); % The test loop size N = 40; tstart = tic(); parfor(ix=1:N) ixstamp = sprintf('Iteration %d at %s\n', ix, datestr(now)); disp(ixstamp); pause(1); end cputime=toc(tstart); toctime= sprintf('Time used is %d seconds', cputime); disp(toctime) %% delete current parallel pool: delete(gcp)
Multi-node parallel MATLAB jobs (16-32 cpus)¶
For running multi-processor MATLAB jobs that involve 16+ cpus and more than a single node, MATLAB Distributed Computer Engine (licensed software) is used, with currently 32 licenses available on Pegasus. These jobs must be submitted to the **parallel* queue with the appropriate ptile resource distribution.* For more information about queue and resource distribution requirements, see Scheduling Jobs.
The parallel LSF MATLAB cluster also needs to be configured. After loading the matlab module, import the default LSF parallel configuration as following:
[username@pegasus ~]$ matlab -nodisplay -r "parallel.importProfile('/share/opt/MATLAB/etc/LSF1.settings');exit";reset
This command only needs to be run once. It imports the cluster profile
named ‘LSF1’ that is configured to use up to 32 MatlabWorkers and to
submit MATLAB jobs to the parallel pegasus queue. This profile does
not have a
projetID associated with the job, and you may need to
coordinate the project name for the LSF job submission. This can be done
by running the following script (only once!) during your matlab session:
%% conf_lsf1_project_id.m %% Verify that LSF1 profile exists, and indicate the current default profile: [allProfiles,defaultProfile] = parallel.clusterProfiles() %% Define the current cluster object using LSF1 profile myCluster=parcluster('LSF1') %% View current submit arguments: get(myCluster,'SubmitArguments') %% Set new submit arguments, change projectID below to your current valid project: set(myCluster,'SubmitArguments','-q general -P projectID') %% Save the cluster profile: saveProfile(myCluster) %% Set the 'LSF1' to be used as a default cluster profile instead of a 'local' parallel.defaultClusterProfile('LSF1'); %% Verify the current profiles and the default: [allProfiles,defaultProfile] = parallel.clusterProfiles()
The above script also reviews your current settings of the cluster
profiles. You can now use the cluster profile for distributed
calculations on up to 32 cpus, for example, to create a pool of
MatlabWorkers for a
%========================================================= % dce_example.m % Distributed Computing Engine (DCE) % Example: Print datestamp within a parallel "parfor" loop %========================================================= myCluster=parcluster('LSF1') % Maximum number of MatlabWorkers is 32 (number of MATLAB DCE Licenses) parpool(myCluster,32); % The test loop size N = 40; tstart = tic(); parfor(ix=1:N) ixstamp = sprintf('Iteration %d at %s\n', ix, datestr(now)); disp(ixstamp); pause(1); end cputime=toc(tstart); toctime= sprintf('Time used is %d seconds', cputime); disp(toctime) delete(gcp)
Please see MATLAB documentation on more ways to parallelize your code.
There may be other people running Distributed Computing Engine and thus using several licenses. Please check the license count as following (all in a single line):
[username@pegasus ~]$ /share/opt/MATLAB/R2013a/etc/lmstat -S MLM -c /share/opt/MATLAB/R2013a/licenses/network.lic
Find the information about numbers of licenses used for the “Users of MATLAB_Distrib_Comp_Engine”, “Users of MATLAB”, and “Users of Distrib_Computing_Toolbox”.
Note on Matlab cluster configurations¶
After importing the new cluster profile, it will remain in your
available cluster profiles. Validate using the
parallel.clusterProfiles() function. You can create, change, and
save profiles using
SaveAsProfile methods on a
cluster object. In the examples, “myCluster” is the cluster object. You
can also create, import, export, delete, and modify the profiles through
the “Cluster Profile Manager” accessible via MATLAB menu in a graphical
interface. It is accessed from the “HOME” tab in the GUI desktop window
under “ENVIRONMENT” section: ->“Parallel”->“Manage Cluster Profiles”
You can also create your own LSF configuration from the Cluster Profile Manager. Choose “Add”->“Custom”->“3RD PARTY CLUSTER PROFILE”->“LSF” as shown below:
… and configure to your needs: