
Licenses are checked out automatically when you use a MATLAB or its toolboxes. Licenses are returned when your sinterative session ends. There is no maximum number of MATLAB sessions a single user can run. However, as more than one instance of Matlab can run within a single interactive session, users must make sure they request increased resource allocation for their interactive session if more than one Matlab instance will be launched.
As with other applications on the HPC systems, MATLAB is managed using environment modules. To see which versions of MATLAB and thirdparty toolboxes are available, type:
[user@cn1234 ~]$ module avail matlab  /usr/local/lmod/modulefiles  matlabFiji/1.50a matlabeeglab/14.1.2 (D) matlabxjview/8.14 matlabFiji/1.51n (D) matlabkilosort/8738ef7 matlab.all/2015a matlabMBEToolbox/3.0 matlabnpy/524bd14 matlab.all/2016b matlabMIToolbox/3.0.2 matlabpronto/2.0.1 matlab.all/2017b matlabPGEToolbox/3.1 matlabsigprofiler/2.3.2.0 matlab.all/2018a (D) matlabSinCHet/1.0 matlabspm/12 matlab.all/2018b matlabalexnet/1.0 matlabspm/12.6906 matlab/2015a matlabcaffe/1.0.0rc3 matlabspm/12.7219 (D) matlab/2016b matlabcaffe/1.0.0 (D) matlabtimestudio/3.18 matlab/2017b matlabconn/17a matlabvgg/17.1.0 matlab/2018a (D) matlabeeglab/13.4.4b matlabwtsimf/1.4 matlab/2018b (L) Where: L: Module is loaded D: Default Module Use "module spider" to find all possible modules. Use "module keyword key1 key2 ..." to search for all possible modules matching any of the "keys". [user@cn1234 ~]$
To select a module, type:
[user@cn1234 ~]$ module load matlab/[ver]
where [ver] is an optional version specification. Without [ver] the default MATLAB version is loaded.
Please note that, for 2017a and later, the complete set of thirdparty toolboxes will not be automatically loaded unless you choose the matlab.all module or load them individually.
To load a thirdparty toolbox, like SPM, for use with MATLAB >= 2017a, do:[user@cn1234 ~]$ module load matlabspm
If you have already begun a MATLAB session, there is no need to restart. The loading can be accomplished using the nih_matmod function:
>> nih_matmod avail Fiji/1.50a alexnet/1.0 kilosort/8738ef7 spm/12.7219 (L,D) Fiji/1.51n (D) caffe/1.0.0rc3 npy/524bd14 timestudio/3.18 MBEToolbox/3.0 caffe/1.0.0 (D) pronto/2.0.1 vgg/17.1.0 MIToolbox/3.0.2 conn/17a sigprofiler/2.3.2.0 wtsimf/1.4 PGEToolbox/3.1 eeglab/13.4.4b spm/12 xjview/8.14 SinCHet/1.0 eeglab/14.1.2 (D) spm/12.6906 Where: D: Default Module L: Module is loaded Use "module spider" to find all possible modules. Use "module keyword key1 key2 ..." to search for all possible modules matching any of the "keys". >> nih_matmod load spm
Use help nih_matmod from within MATLAB for complete usage information.
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Because MATLAB is not permitted on the Biowulf login node, users must request resources on a compute node.
First, establish an ssh or XWindows connection to the Biowulf login node. When running an XWindows session on Biowulf, remember to use the X or Y option with your ssh command.
Then use the sinteractive command to request resources on a compute node:
[user@biowulf ~]$ sinteractive salloc.exe: Pending job allocation 15323416 salloc.exe: job 15323416 queued and waiting for resources salloc.exe: job 15323416 has been allocated resources salloc.exe: Granted job allocation 15323416 salloc.exe: Waiting for resource configuration salloc.exe: Nodes cn1640 are ready for job [user@cn1640 ~]$
By default, the sinteractive command only allocates a few cpus and a small amount of memory. You can request more cpus and memory with the cpuspertask=ncpus and mem=GBg options respectively. See sinteractive h for a complete list of options.
To run the MATLAB integrated development environment (IDE also known as the Java virtual machine), an X Windows connection is required. NoMachine's NX is the XWindows client currently recommended by staff for Windows and Mac. When used to start a GNOME desktop session, NX is optimized to provide a fast, responsive MATLAB environment.
HINT: Debugging XWindows clients
If you have trouble starting the MATLAB IDE, ensure that your XWindows client is working properly by typing 'xclock' in the shell. This shouldn't be an issue with a GUI based client like NX.
After connecting to a compute node, load the MATLAB module.
[user@cn1234 ~]$ module load matlab [user@cn1234 ~]$ matlab&
Including & in your command above allows you to continue using the terminal while the MATLAB application is running. You should now see a MATLAB splash screen followed by the IDE:
It is also possible to run MATLAB interactively on the commandline, without the IDE. This would be useful if you do not wish to use XWindows.
[user@cn1234 ~]$ module load matlab [user@cn1234 ~]$ matlab nodisplay < M A T L A B (R) > Copyright 19842021 The MathWorks, Inc. R2021a Update 3 (9.10.0.1684407) 64bit (glnxa64) May 27, 2021 To get started, type doc. For product information, visit www.mathworks.com. >> quit [user@cn1234 ~]$
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The simplest (but perhaps least useful) way to run a MATLAB job in the background would be to hardcode variables and MATLAB commands directly into a script. Even though this method is not very practical it serves as a useful example to MATLAB users unfamiliar with bash scripting:
#!/bin/bash # this file is called hyp1.sh module load matlab matlab nojvm<<EOF a = 3; b = 4; H = sqrt(a^2 + b^2) exit EOF
You could run this job in the background of an interactive session like so:
[user@cn0001 ~]$ ./hyp1.sh > hyp1_output 2>&1 &
HINT: Permission denied error
If you receive an error such as this:
bash: ./hyp1.sh: Permission denied
Use the chmod command to make the file executable, like so:
[user@cn0001]$ chmod 755 hyp1.sh
In this case, the output will be redirected to hyp1_output. The computation will proceed in the background allowing the you to run another session of MATLAB interactively or run other MATLAB programs in the background. If you use this strategy to run a few instances of MATLAB in parallel you must be careful not to overload your allocated CPUs.
HINT:`EOF' warning
If a message similar to the following is found in your output file, make sure to remove any trailing whitespace from both of the EOF lines.
/var/spool/slurm/slurmd/job48226/slurm_script: line 10: warning: heredocument at line 5 delimited by endoffile (wanted `EOF')
You can set up a batch script that contains MATLAB commands and pass variables to the job at the time of submission. This might be useful for very simple MATLAB analyses. For example:
#!/bin/bash # this file is called hyp2.sh a=$1 b=$2 echo "a is $a, b is $b"; module load matlab matlab nojvm<<EOF H = sqrt($a^2 + $b^2) exit EOF
In this script the values of a and b are expected to be passed from the command line. This is accomplished like so:
[user@cn0001 ~]$ ./hyp2.sh 22 5 > hyp2_output 2>&1 &
Similar to the previous example, the output is directed to hyp2_output.
Of course it is possible to write your own functions and call them in a shell script using the same syntax as above. Let's assume you write a function and save it in hyp.m
function H = hyp(a,b) % return the hypotenuse (H) % from the legs (a and b) % convert chars to doubles % (this is necessary when % code is compiled) if ischar(a), a = str2double(a); end if ischar(b), b = str2double(b); end H = sqrt(a^2 + b^2)
To run the code in the background, you could write a script like so:
#!/bin/bash # this file is called hyp3.sh a=$1 b=$2 module load matlab matlab nojvm<<EOF cd /full/path/to/hyp.m hyp($a,$b); exit EOF
Similar to the last example you would run this code in the backround with:
[user@cn0001 ~]$ ./hyp3.sh 8 4 > hyp3_output 2>&1 &
This method allows you to develop analyses of any complexity in MATLAB (rather than hardcoding commands into shell scripts) and run them in the background of an interactive session.
One common approach is to write a MATLAB function that accepts a file name as input, loads the file (containing all variables and data), performs some analysis, and then saves the analyzed data to a new .mat file. This minimizes the complexity of the input that must be supplied through a shell script. In this case, the _output file will only be used for diagnosing a debugging problems, because the output will be saved to a new .mat file specified by the user in the function.
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Long running jobs, or jobs that are "embarrassingly parallel" should be submitted to the batch system. To submit matlab code as a batch job, you should write another a shell script as follows:
#!/bin/bash # this file is called hyp4.sh #SBATCH jobname=hypotenuse4 #SBATCH mailtype=BEGIN,END module load matlab matlab nodisplay nodesktop nojvm nosplash r 'cd /data/user; hyp(3,4); exit;'
Note the optional inclusion of #SBATCH options that set the job name and email alerts. You would then submit the job like so:
[user@biowulf ~]$ sbatch /data/user/hyp4.sh
For more information on using the sbatch command for SLURM, visit the Job Submission section of the Biowulf User Guide.
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The swarm program is a convenient way to submit large numbers of jobs. With swarm you can run over 1,000 instances of your compiled MATLAB code distributed across the cluster. You create a swarm command file containing a single line for each independent job. The swarm program will then package up the commands into batch jobs and submit them to Slurm for you.
To run a swarm based on the example above, create a swarm command file named hyp.swarm with each line containing a single command:
matlab nodisplay nodesktop nojvm nosplash r 'cd /data/${USER}; hyp(3,4); exit;' matlab nodisplay nodesktop nojvm nosplash r 'cd /data/${USER}; hyp(5,6); exit;' matlab nodisplay nodesktop nojvm nosplash r 'cd /data/${USER}; hyp(7,8); exit;'
Submit this file to the batch system with the command:
[user@biowulf ~]$ swarm f hyp.swarm module=matlab
Please note that, with this type of swarm, subjob packing (p option) should not be used. Bundling (b), however, would work as expected.
Several toolboxes can use GPU resources. Some of those toolboxes are: Statistics and Machine Learning, Image processing, Deep Learning, Computer Vision, Signal Processing, Wavelet, Curve Fitting, Parallel Computing. Many functions in the Deep Learning Toolbox use GPU resources automatically (see Matlab's Deep Learning with GPUs). For all other toolboxes users must explicitly elect to use GPU resources by passing a gpuArray data structure to a given function. For further details on how to use GPU with Matlab, please see Matlab's Help Center section on how to Run Matlab Functions on a GPU.
The MATLAB Parallel Computing Toolbox enables you to develop distributed and parallel MATLAB applications and execute them using multiple cores in a single node or to utilize the graphical processing units (GPUs) of a properly equipped machine.
Please NOTE:
1. In it's current configuration, the Parallel Computing Toolbox does not scale beyond a single node. This will allow your job to run up to 16 times faster on the norm partition. This may be sufficient for many jobs. But this toolbox will not allow you to run jobs on multiple nodes. To run jobs of larger scale, use sbatch or swarm.
2. Within MATLAB and in online documentation this toolbox is referred to as the Parallel Computing Toolbox. Within the licensing software it is referred to as the Distributed Processing Toolbox. There is a related Mathworks product that is not currently installed on our systems that extends the functionality of the Parallel Computing Toolbox called the Distributed Computing Server. This has been the source of confusion.
See MATLAB Parallel Computing Toolbox for examples on using the toolbox on biowulf.
back to topSeveral 3rd party toolboxes have been installed centrally in some versions of MATLAB:
The centrally installed SPM12 Toolbox itself contains the following toolboxes: