# MATLAB¶

## Introduction¶

Notes

Since 2016, the MATLAB module is not updated anymore and purchase of new licenses is not planned. However, due to e-infra integration, IT4Innovations may have access to recent MATLAB versions from cooperating organizations in the future. More information will be available in May, 2021.

MATLAB is available in versions R2015a and R2015b. There are always two variants of the release:

- Non-commercial or so-called EDU variant, which can be used for common research and educational purposes.
- Commercial or so-called COM variant, which can used also for commercial activities. Commercial licenses are much more expensive, so usually the commercial license has only a subset of features compared to the available EDU license.

To load the latest version of MATLAB load the module:

```
$ ml MATLAB
```

The EDU variant is marked as default. If you need other version or variant, load the particular version. To obtain the list of available versions, use:

```
$ ml av MATLAB
```

If you need to use the MATLAB GUI to prepare your MATLAB programs, you can use MATLAB directly on the login nodes. However, for all computations, use MATLAB on the compute nodes via PBS Pro scheduler.

If you require the MATLAB GUI, follow the general information about running graphical applications.

MATLAB GUI is quite slow using the X forwarding built in the PBS (`qsub -X`

), so using X11 display redirection either via SSH or directly by `xauth`

(see the GUI Applications on Compute Nodes over VNC section) is recommended.

To run MATLAB with GUI, use:

```
$ matlab
```

To run MATLAB in text mode, without the MATLAB Desktop GUI environment, use:

```
$ matlab -nodesktop -nosplash
```

plots, images, etc. will be still available.

## Running Parallel MATLAB Using Distributed Computing Toolbox / Engine¶

Distributed toolbox is available only for the EDU variant

The MPIEXEC mode available in previous versions is no longer available in MATLAB 2015. In addition, the programming interface has changed. Refer to Release Notes.

Delete previously used file mpiLibConf.m, we have observed crashes when using Intel MPI.

To use Distributed Computing, you first need to setup a parallel profile. We have provided the profile for you, you can either import it in the MATLAB command line:

```
> parallel.importProfile('/apps/all/MATLAB/2015b-EDU/SalomonPBSPro.settings')
ans =
SalomonPBSPro
```

or in the GUI, go to tab *HOME -> Parallel -> Manage Cluster Profiles...*, click *Import* and navigate to:

/apps/all/MATLAB/2015b-EDU/SalomonPBSPro.settings

With the new mode, MATLAB itself launches the workers via PBS, so you can use either an interactive mode or a batch mode on one node, but the actual parallel processing will be done in a separate job started by MATLAB itself. Alternatively, you can use a "local" mode to run parallel code on just a single node.

### Parallel MATLAB Interactive Session¶

The following example shows how to start the interactive session with support for MATLAB GUI. For more information about GUI based applications, see this page.

```
$ xhost +
$ qsub -I -v DISPLAY=$(uname -n):$(echo $DISPLAY | cut -d ':' -f 2) -A NONE-0-0 -q qexp -l select=1 -l walltime=00:30:00 -l license__matlab-edu__MATLAB=1
```

This `qsub`

command example shows how to run MATLAB on a single node.

The second part of the command shows how to request all necessary licenses. In this case, 1 MATLAB-EDU license and 48 Distributed Computing Engines licenses.

Once the access to compute nodes is granted by PBS, the user can load following modules and start MATLAB:

```
$ ml MATLAB/2015a-EDU
$ matlab &
```

### Parallel MATLAB Batch Job in Local Mode¶

To run MATLAB in a batch mode, write a MATLAB script, then write a bash jobscript and execute via the `qsub`

command. By default, MATLAB will execute one MATLAB worker instance per allocated core.

```
#!/bin/bash
#PBS -A PROJECT ID
#PBS -q qprod
#PBS -l select=1:ncpus=24:mpiprocs=24:ompthreads=1
# change to shared scratch directory
SCR=/scratch/.../$USER/$PBS_JOBID # change path in according to the cluster
mkdir -p $SCR ; cd $SCR || exit
# copy input file to scratch
cp $PBS_O_WORKDIR/matlabcode.m .
# load modules
ml MATLAB/2015a-EDU
# execute the calculation
matlab -nodisplay -r matlabcode > output.out
# copy output file to home
cp output.out $PBS_O_WORKDIR/.
```

This script may be submitted directly to the PBS workload manager via the `qsub`

command. The inputs and the MATLAB script are in the matlabcode.m file, outputs in the output.out file. Note the missing .m extension in the `matlab -r matlabcodefile`

call, **the .m must not be included**. Note that the **shared /scratch must be used**. Further, it is **important to include the quit** statement at the end of the matlabcode.m script.

Submit the jobscript using `qsub`

:

```
$ qsub ./jobscript
```

### Parallel Matlab Local Mode Program Example¶

The last part of the configuration is done directly in the user's MATLAB script before Distributed Computing Toolbox is started.

```
cluster = parcluster('local')
```

This script creates the scheduler object *cluster* of the type *local* that starts workers locally.

Hint

Every MATLAB script that needs to initialize/use `matlabpool`

has to contain these three lines prior to calling the `parpool(sched, ...)`

function.

The last step is to start `matlabpool`

with the *cluster* object and a correct number of workers. We have 24 cores per node, so we start 24 workers.

```
parpool(cluster,24);
... parallel code ...
parpool close
```

The complete example showing how to use Distributed Computing Toolbox in local mode is shown here.

```
cluster = parcluster('local');
cluster
parpool(cluster,24);
n=2000;
W = rand(n,n);
W = distributed(W);
x = (1:n)';
x = distributed(x);
spmd
[~, name] = system('hostname')
T = W*x; % Calculation performed on labs, in parallel.
% T and W are both codistributed arrays here.
end
T;
whos % T and W are both distributed arrays here.
parpool close
quit
```

You can copy and paste the example in a .m file and execute. Note that the `parpool`

size should correspond to the **total number of cores** available on allocated nodes.

### Parallel MATLAB Batch Job Using PBS Mode (Workers Spawned in a Separate Job)¶

This mode uses the PBS scheduler to launch the parallel pool. It uses the SalomonPBSPro profile that needs to be imported to Cluster Manager, as mentioned before. This method uses MATLAB's PBS Scheduler interface - it spawns the workers in a separate job submitted by MATLAB using qsub.

This is an example of an m-script using the PBS mode:

```
cluster = parcluster('SalomonPBSPro');
set(cluster, 'SubmitArguments', '-A OPEN-0-0');
set(cluster, 'ResourceTemplate', '-q qprod -l select=10:ncpus=24');
set(cluster, 'NumWorkers', 240);
pool = parpool(cluster,240);
n=2000;
W = rand(n,n);
W = distributed(W);
x = (1:n)';
x = distributed(x);
spmd
[~, name] = system('hostname')
T = W*x; % Calculation performed on labs, in parallel.
% T and W are both codistributed arrays here.
end
whos % T and W are both distributed arrays here.
% shut down parallel pool
delete(pool)
```

Note that we first construct a cluster object using the imported profile, then set some important options, namely: `SubmitArguments`

, where you need to specify accounting id, and `ResourceTemplate`

, where you need to specify the number of nodes to run the job.

You can start this script using the batch mode the same way as in the Local mode example.

### Parallel MATLAB Batch With Direct Launch (Workers Spawned Within the Existing Job)¶

This method is a "hack" invented by us to emulate the `mpiexec`

functionality found in previous MATLAB versions. We leverage the MATLAB Generic Scheduler interface, but instead of submitting the workers to PBS, we launch the workers directly within the running job, thus we avoid the issues with master script and workers running in separate jobs (issues with license not available, waiting for the worker's job to spawn, etc.)

Warning

This method is experimental.

For this method, you need to use the SalomonDirect profile, import it using the same way as SalomonPBSPro.

This is an example of an m-script using direct mode:

```
parallel.importProfile('/apps/all/MATLAB/2015b-EDU/SalomonDirect.settings')
cluster = parcluster('SalomonDirect');
set(cluster, 'NumWorkers', 48);
pool = parpool(cluster, 48);
n=2000;
W = rand(n,n);
W = distributed(W);
x = (1:n)';
x = distributed(x);
spmd
[~, name] = system('hostname')
T = W*x; % Calculation performed on labs, in parallel.
% T and W are both codistributed arrays here.
end
whos % T and W are both distributed arrays here.
% shut down parallel pool
delete(pool)
```

### Non-Interactive Session and Licenses¶

If you want to run batch jobs with MATLAB, be sure to request appropriate license features with the PBS Pro scheduler, at least the `-l license__matlab-edu__MATLAB=1`

for the EDU variant of MATLAB. For more information about how to check the license features states and how to request them with PBS Pro, look here.

In case of non-interactive session, read the following information on how to modify the `qsub`

command to test for available licenses prior getting the resource allocation.

### MATLAB Distributed Computing Engines Start Up Time¶

Starting MATLAB workers is an expensive process that requires certain amount of time. For more information, see the following table:

compute nodes | number of workers | start-up time[s] |
---|---|---|

16 | 384 | 831 |

8 | 192 | 807 |

4 | 96 | 483 |

2 | 48 | 16 |

## MATLAB on UV2000¶

The UV2000 machine available in the qfat queue can be used for MATLAB computations. This is an SMP NUMA machine with a large amount of RAM, which can be beneficial for certain types of MATLAB jobs. CPU cores are allocated in chunks of 8 for this machine.

You can use MATLAB on UV2000 in two parallel modes:

### Threaded Mode¶

Since this is an SMP machine, you can completely avoid using Parallel Toolbox and use only MATLAB's threading. MATLAB will automatically detect the number of cores you have allocated and will set `maxNumCompThreads`

accordingly and certain operations, such as fft, eig, svd, etc. will be automatically run in threads. The advantage of this mode is that you do not need to modify your existing sequential codes.

### Local Cluster Mode¶

You can also use Parallel Toolbox on UV2000. Use local cluster mode, the "SalomonPBSPro" profile will not work.