# MATLAB

## On this page

MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language. Developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, and Fortran.

### Versions

We currently offer the following MATLAB versions:

You can query the versions yourself with `module av matlab`

.

#### Usage

Load the most recent version available with:

You can also load a particular version with `module load math/MATLAB/<version>`

, e.g. `module load math/MATLAB/2017a`

See the vendor documentation: https://www.mathworks.com/help/matlab/

#### MATLAB Version 2021b

If the **Help Improve MATALAB** feature is enabled, MATLAB returns an error on `exit`

if part of the MATLAB preferences system was not initialized before exit.

E.g.:

To work around this issue, you can ensure that the settings are initialized properly by calling:

in your MATLAB code. Alternatively, you can **uncheck** the `Improve MATLAB by sending user experience information to MathWorks`

option under **Preferences -> MATLAB -> General**.

### Toolboxes

The currently available toolboxes are

Toolbox | Version |
---|---|

Curve Fitting Toolbox | 3.5.8 |

Econometrics Toolbox | 5.1 |

Fuzzy Logic Toolbox | 2.4 |

Image Acquisition Toolbox | 5.5 |

Image Processing Toolbox | 10.3 |

MATLAB Compiler | 7.0 |

MATLAB Compiler SDK | 6.6 |

Mapping Toolbox | 4.7 |

Optimization Toolbox | 8.2 |

Parallel Computing Toolbox | 6.13 |

Partial Differential Equation Toolbox | 3.1 |

Signal Processing Toolbox | 8.1 |

Statistics and Machine Learning Toolbox | 11.4 |

Symbolic Math Toolbox | 8.2 |

Wavelet Toolbox | 5.1 |

### MATLAB Java Crash Dump Files

If you want to prevent MATLAB from spamming your home directory with the `java.log.<number>`

files, you can set the following environment variable:

You can also define this environment variable in your `.bashrc`

file.

# Using Matlab

Using Matlab is possible several ways, which we would like to briefly introduce here.

**Examples**

were mostly taken from the vendor documentation

### Matlab Startup Options

Starting MATLAB from a Linux system prompt using `matlab`

gives you several start-up options. We only present an overview of the most commonly used options. A complete list can be found in the
MATLAB documentation
.

Option | Result |
---|---|

`-nojvm` | Start MATLAB without the JVM software. Features that require Java are not supported. |

`-nodisplay` | Start the JVM software without starting MATLAB desktop. Does not display X commands. Overrides the DISPLAY environment variable. |

`-nosplash` | Do not display the splash screen during startup. |

`-singleCompThread` | Limit MATLAB to a single computational thread. For numerical computations only. (Default) |

`-multiCompThread` | Do not limit MATLAB to a single computational thread. |

`-sd` folder | Set the MATLAB folder, specified as string, e.g. `-sd "~/matlab"` sets the folder to matlab. |

`-r` statement | Execute the statement. Use this option for interactive workflows. Do not use this option with the `-batch` option. |

`-batch` statement | Execute MATLAB script, statement, or function non-interactively. Important features are: `-nodesktop` , `-nosplash` , logs to stdout & stderr. |

`-batch *statement*`

is to be preferred to `-r *statement*`

.## Interactive Job Submission

Request resources according to your needs, e.g. with:

Load the desired Matlab module:

Now you can run Matlab. Add `-nojvm`

flag to start Matlab without the Java virtual machine, `-nosplash`

prevents Matlab from displaying the Matlab logo.

Check the version info and the available toolboxes for that version:

gives:

**The use of the Matlab-Compiler is to be preferred!**

Only a limited number of licences are available on the cluster. It is therefore preferable to use the Matlab-Compiler to compile your scripts beforehand. We explain more about the Matlab-Compiler as follows.

## Matlab-Compiler

There are several options to compile your Matlab code to stand-alone executables/libraries. Being independent of licenses is one of the major advantages here, of course. But when running compiled code with the Matlab Runtime Envirenment (MRE) on MOGON you have to consider the threading of your code just as well as when you run Matlab itself.

#### mcc Command Arguments

Option | Description | Comment |
---|---|---|

`-?` | Display help message | Cannot be used in a `deploytool` app. |

`-m` | Generate a standalone application. | Equivalent to `-W main -T link:exe` . Cannot be used in a `deploytool` app. |

`-n` | Automatically treat numeric inputs as MATLAB doubles. | Cannot be used in a `deploytool` app. |

`-o` outputfile | Specify name of final output file. | Adds appropriate extension. Cannot be used in a `deploytool` app. |

`-R` option | Specify run-time options for MATLAB Runtime. | Valid only for standalone applications using MATLAB Compiler. , `-nodisplay` , `-logfile ` , `-startmsg` , and `-completemsg ` |

`-v` | Verbose; display compilation steps. |

### Single-Threaded

If your code doesn’t need the full multithreading capability, which often is the case, you should compile your code with:

This causes the `-singleCompThread`

option to be applied automatically, which limits MATLAB to a single computational thread. **This is enabled by default on MOGON.**

Using `-singleCompThread`

also ensures that your **standalone code** will run on a single computational thread, which not only doesn’t frustrate the core scheduler and the other users less, but also improves the performance of your code by spending less time scheduling all the threads on a core.

#### Hello MOGON Example

### Multi-Threaded

Generally, Matlab detects the number of physical cores and opens the same amount of threads to make full use of the multithreading implemented in the built-in functions. By calling

you obtain multithread code. Often this might be wanted, but you have to make sure that you select the appropriate resources for this then - namely, the appropriate CPUs. Since Matlab wants to use everything on a host you’ll have to set`#SBATCH <nowiki>--</nowiki>cpus-per-task`

and an appropriate memory reservation.

#### Hello MOGON Example

## Submitting a Serial Matlab Job

We would like to explain with a typical `Hello MOGON!`

example how you can achieve this.

The trivial MATLAB script has only one line:

**Notice:** Save your file with the `.m`

extension, but call it without the `.m`

extension.

A serial Job requires only one core, the Slurm job script `serial_matlab_job.slurm`

therefore would read as follows:

The start-up options (`-singleCompThread`

, `-nodisplay`

, `-nosplash`

) used are explaind above. To run the script, you can now simply submit it to the batch system with the following command:

After the job completes, you can view the output with:

which should give you

as Matlab will not be able to fully utilize the node, but considers every FPU equal to a CPU.

## Submitting a Multi-threaded Matlab Job

The following example is a job that uses Matlab’s Parallel Computing Toolbox . You can benefit from the built-in multithreading provided by Matlab’s BLAS implementation, if your code makes use of this Toolbox or if you have intense computations. The following is an example of MathWorks and can be found here .

The Slurm job script is kept simple, with one process that has four cores for multithreading:

To run the script, simply submit it to the scheduler with the following command:

After the job has finished, view the output with the following command:

which should give you the following output:

For optimal performance, the value of `--cpus-per-task`

must be adjusted. Use the smallest value that gives you a significant performance gain.
The matrix multiplication example from above has the following runtimes on MOGON II with different `--cpus-per-task`

:

CPUs per Task | Runtime (s) |
---|---|

2 | 29.22 |

4 | 17.06 |

6 | 11.43 |

8 | 8.40 |

**JVM**

The Parallel Computing Toolbox requires JVM, please do not specify `-nojvm`

if you want to use it!

## Submitting a Matlab Job on GPUs

Testing computationally intensive operationsFor operations where the number of floating-point computations performed per element read from or written to memory is high, the memory speed is much less important. In this case the number and speed of the floating-point units is the limiting factor. These operations are said to have high “computational density”.

A good test of computational performance is a matrix-matrix multiply. For multiplying two $N \times N$ matrices, the total number of floating-point calculations is $$ FLOPS(N) = 2N^3 - N^2 $$

Two input matrices are read and one resulting matrix is written, for a total of $3N^2$ elements read or written. This gives a computational density of $(2N - 1)/3$ FLOP/element. Contrast this with

`plus`

as used above, which has a computational density of $1/2$ FLOP/element.MATLAB Help Center, Measuring GPU Performance

## How-To: Compiling an m-file via Job

In the introduction on compiling m-Files in this article, you have probably observed that even the simple `Hello MOGON!`

-Funcion took its moment to compile into a stand-alone C application. With larger scripts, of course, compile time will not improve, which is why you should always compile m-File in a job on the localscratch directory.

The following is a short, simple example demonstrating how to create such a Job that compiles m-files on the localsratch directory.

### Steps

Start by creating a new folder and change to it, e.g.:

You can simple delete the entire folder after working through this How-To.

We will re-use the familiar `Hello MOGON!`

-script from the previous examples:

The job script is a little bit more complex. In the first lines, the usual and necessary job options are given. With the line `#SBATCH --signal=B:SIGUSR2@60`

we send the signal `SIGUSR2`

to the application one minute before the job reaches its walltime. We then define variables for the current working directory and an ouput folder where we want to save our resulting files. After loading the proper MATLAB module, we define the cleanup function, which simply copies all files from the localscratch directory to the output directory, either after the job completed successfully or one minute before the job reaches its time limit. The subsequent lines only create folders or changes into them. The actual compilation takes place in the line `mcc -m ...`

already on the localscratch directory of the compute node. Of course, you can provide the compiler with all the options that are given above or the ones you can find in the MATLAB documentation.

Submit the job as usual with the following command:

Our `cleanup`

function copied all results to the output directory, after the stand-alone application has been created, run it as follows:

The output on the command line should look similar like this: