There are two ways of using MATLAB on Compute Canada clusters.
- Running MATLAB directly
This approach requires you to have access to a MATLAB license. You may either:
- Run MATLAB on Cedar or Béluga, both of which have a license available for any student, professor or academic researcher.
- Use an external license, that is, one owned by your institution, faculty, department, or lab. See "Using an external license" below.
- Running a compiled MATLAB application
This method requires compiling your code into a binary using the MATLAB Compiler (mcc). You can then run that binary executable using the appropriate MATLAB Runtime.
More details about these approaches are provided below.
Using an external license
Compute Canada is a hosting provider for MATLAB. This means that we have MATLAB installed on our clusters and can allow you to access an external license to run computations on our infrastructure. Arrangements have already been made with several Canadian institutions to make this automatic. To see if you already have access to a license, carry out the following test:
[name@cluster ~]$ module load matlab/2018a [name@cluster ~]$ matlab -nodisplay -nojvm -r "fprintf('%s\n', license()); exit" < M A T L A B (R) > Copyright 1984-2018 The MathWorks, Inc. R2018a (18.104.22.1683654) 64-bit (glnxa64) February 23, 2018 987654 [name@cluster ~]$
If any license number is printed, you're okay. Be sure to run this test on each cluster on which you want to use MATLAB; you may get different results.
If you get a message, "This version is newer than the version of the license.dat file and/or network license manager on the
server machine", then try an older version of MATLAB in the
module load line.
Otherwise, either your institution does not have a MATLAB license, does not allow its use in this way, or no arrangements have yet been made. Find out who administers the MATLAB license at your institution (faculty, department) and contact them, or your Mathworks account manager, to establish that you are allowed to use the license in this way.
If you are allowed, then some technical configuration will be required. Create a file similar to the following example:
# MATLAB license passcode file SERVER <ip address> ANY <port> USE_SERVER
and put it in the directory $HOME/.licenses/, where the IP address and port number correspond to the values for your campus license server. Next you will need to ensure that the license server on your campus is reachable by our compute nodes. This will require our technical team to get in touch with the technical people managing your license software. Please write to technical support, so that we can arrange this for you.
For online documentation, see http://www.mathworks.com/support For product information, visit www.mathworks.com.
Preparing your .matlab folder
Because the /home directory is accessible in read-only mode on some clusters' compute nodes, users should create a .matlab symbolic link that makes sure that the MATLAB profile and job data will be written to the /scratch space instead:
[name@cluster ~]$ cd $HOME [name@cluster ~]$ if [ -d ".matlab" ]; then mv .matlab scratch/ else mkdir -p scratch/.matlab fi && ln -sn scratch/.matlab .matlab
Running a MATLAB code
Important: Any MATLAB calculation larger than a short test job of, say, 5 minutes, must be submitted to the scheduler. For instructions on using the scheduler, please see the Running jobs page.
Consider the following example code:
function cosplot() % MATLAB file example to approximate a sawtooth % with a truncated Fourier expansion. nterms=5; fourbypi=4.0/pi; np=100; y(1:np)=pi/2.0; x(1:np)=linspace(-2.0*pi,2*pi,np); for k=1:nterms twokm=2*k-1; y=y-fourbypi*cos(twokm*x)/twokm^2; end plot(x,y) print -dpsc matlab_test_plot.ps quit end
Here is a simple Slurm script that you can use to run
#!/bin/bash -l #SBATCH --job-name=matlab_test #SBATCH --account=def-someprof # adjust this to match the accounting group you are using to submit jobs #SBATCH --time=0-03:00 # adjust this to match the walltime of your job #SBATCH --nodes=1 #SBATCH --ntasks=1 #SBATCH --cpus-per-task=1 # adjust this if you are using parallel commands #SBATCH --mem=4000 # adjust this according to the memory requirement per node you need #SBATCH --email@example.com # adjust this to match your email address #SBATCH --mail-type=ALL # Choose a version of MATLAB by loading a module: module load matlab/2018a # Remove -singleCompThread below if you are using parallel commands: srun matlab -nodisplay -singleCompThread -r "cosplot"
Submit the job using sbatch:
[name@server ~]$ sbatch matlab_slurm.sl
Do not use the
-singleCompThread option if you request
more than one core with
You should also ensure that the size of your MATLAB parpool
matches the number of cores you are requesting.
Each time you run MATLAB, it will create a file like
java.log.12345 unless you supply the
-nojvm may interfere with certain plotting functions.
For further information on command line options
see MATLAB (Linux) on the MathWorks website.
Running multiple parallel MATLAB jobs simultaneously
There is a known issue when two (or more) parallel MATLAB jobs are initializing their
parpool simultaneously : multiple new MATLAB instances are trying to read and write to the same
.dat file in the
$HOME/.matlab/local_cluster_jobs/R* folder, which corrupts the local parallel profile used by other MATLAB jobs. To fix the corrupted profile, delete the
local_cluster_jobs folder when no job is running.
There are two main definitive solutions:
- Making sure only one MATLAB job at a time will start its
parpool. There are many possible technical solutions, but none is perfect:
- using a lock file (which may remain locked if a previous job has failed),
- using random delays (which may be equal or almost equal, and still cause the corruption),
- using always increasing delays (which are wasting compute time),
- using Slurm options
--dependency=after:JOBIDto control the start time (which increases wait time in the queue).
- Making sure each MATLAB job will create a local parallel profile in a unique location of the filesystem.
In your MATLAB code:
% Create a "local" cluster object local_cluster = parcluster('local') % Modify the JobStorageLocation to $SLURM_TMPDIR local_cluster.JobStorageLocation = getenv('SLURM_TMPDIR') % Start the parallel pool parpool(local_cluster);
- FAS Research Computing, MATLAB Parallel Computing Toolbox simultaneous job problem
- MathWorks, ... from multiple MATLAB sessions that use a shared preference directory
Using the Compiler and Runtime libraries
Important: Like any other intensive job, you must always run MCR code within a job that you will have submitted to the scheduler. For instructions on using the scheduler, please see the Running jobs page.
You can also compile your code using MATLAB Compiler, included among the Compute Canada-hosted modules. See documentation for the compiler on the MathWorks website. At the moment, mcc is provided for versions 2014a, 2018a and later.
To compile the
cosplot.m example given above, you would use the command
[name@yourserver ~]$ mcc -m -R -nodisplay cosplot.m
This will produce a binary named cosplot, as well as a wrapper script. To run the binary on Compute Canada servers, you will only require the binary. The wrapper script, named run_cosplot.sh, will not work as is on our servers, because MATLAB assumes that some libraries can be found in specific locations. Instead, we provide a different wrapper script, called run_mcr_binary.sh which sets the correct paths.
On one of our servers, load an MCR module corresponding to the MATLAB version you used to build the executable:
[name@server ~]$ module load mcr/R2018a
Run the following command:
[name@server ~]$ setrpaths.sh --path cosplot
then, in your submission script (not on the login nodes), use your binary as so:
You will only need to run the setrpaths.sh command once for each compiled binary. The run_mcr_binary.sh will instruct you to run it if it detects that it has not been done.