# Licences

## No Licence?

If you want to run MATLAB code on the cluster, but are not a member of an institution without access to floating licences, MATLAB code can still be run on the cluster using MCR.

# Example script

## Note

When developing MATLAB code on your local machine, take measures to ensure it will be platform independent. Use relative paths when possible and not avoid using '\'s see here.

## Script Example

```
#!/bin/bash -e
#SBATCH --job-name MATLAB_job # Name to appear in squeue
```

#SBATCH --time 01:00:00 # Max walltime

#SBATCH --mem 512MB # Max memory

module load MATLAB/2018b

# Run the MATLAB script MATLAB_job.m

matlab -nodisplay < MATLAB_job.m

## Function Example

```
#!/bin/bash -e
#SBATCH --job-name MATLAB_job # Name to appear in squeue
```

#SBATCH --time 06:00:00 # Max walltime

#SBATCH --mem 2048MB # Max memory

#SBATCH --cpus-per-task 4 # 2 physical cores.

#SBATCH --output %x.log # Location of output log

module load MATLAB/2018b

#Job run

matlab -nodisplay -r "addpath(genpath('../parentDirectory'));myFunction(5,20)"

## Command Line

When using matlab on command line, all flag options use a single '

`-`

' e.g.`-nodisplay`

, this differs from the GNU convention of using`--`

for command line options of more than one character.

## Bash in MATLAB

Using the prefix

`!`

will allow you to run bash commands from within MATLAB. e.g.`!squeue -u $USER`

will print your currently queued slurm jobs.

# Parallelism

MATLAB does not support MPI therefore #SBATCH --ntasks should always be 1, but if given the necessary resources some MATLAB functions can make use of multiple threads (--cpus-per-task) or GPUs (--gres gpu).

## Implicit parallelism.

Implicit parallelism requires no changes to be made in your code. By default MATLAB will utilise multi-threading for a wide range of operations, scalability will vary but generally you will not be able to utilise more than a 4-8 CPUs this way.

## Explicit parallelism.

Explicit parallelism is when you write your code specifically to make use of multiple CPU's. This can be done using MATLABs *parpool*-based language constructs, MATLAB assigns each thread a 'worker' that can be assigned sections of code.

MATLAB will make temporary files under your home directory (in ~/.matlab/local_cluster_jobs) for communication with worker processes. To prevent simultaneous parallel MATLAB jobs from interfering with each other you should tell them to each use their own job-specific local directories:

```
pc = parcluster('local')
pc.JobStorageLocation = getenv('TMPDIR')
parpool(pc, str2num(getenv('SLURM_CPUS_PER_TASK')))
```

## Note

Parpool will throw a warning when started due to a difference in how time zone is specified. To fix this, add the following line to your SLURM script:

`export TZ="Pacific/Auckland'`

The main ways to make use of parpool are;

**parfor: **Executes each iteration of a loop on a different worker. e.g.

`parfor i=1:100`

%Your operation here.

end

`parfor`

operates similarly to a SLURM job array and must be embarrassingly parallel. Therefore all variables either need to be defined locally (used internally within one iteration of the loop), or static (not changing during execution of loop).

More info here.

**parfeval:**

`parfeval`

is used to assign a particular function to a thread, allowing it to be run asynchronously. e.g.

`my_coroutine=parfeval(@my_async_function,2,in1,in2);`

% Do something that doesn't require outputs from 'my_async_function'

[out1, out2]=fetchOutputs(my_coroutine); % If 'my_coroutine' has not finished execution will pause.

function [out1,out2]=my_async_function(in1,in2)

%Your operation here.

end

`fetchOutputs`

is used to retrieve the values.

More info here.

## Note

When killed (cancelled, timeout, etc), job steps utilising parpool may show state

`OUT_OF_MEMORY`

, this is a quirk of how the steps are ended and not necessarily cause to raise total memory requested.

Determining which of these categories your variables fall under is a good place to start when attempting to parallelise your code.

## Tip

If your code is parallel at a high level it is preferable to use SLURM job arrays as there is less computational overhead and the multiple smaller jobs will queue faster.

# Using GPUs

As with standard parallelism, some MATLAB functions will work implicitly on GPUs while other require setup. More info on using GPUs with MATLAB here, and writing code for GPUs here.

MATLAB uses Nvidia CUDA drivers, so make sure to include `module load CUDA`

before launching MATLAB.

## GPU Example

```
#!/bin/bash -e
#SBATCH --job-name MATLAB_GPU # Name to appear in squeue
```

#SBATCH --time 06:00:00 # Max walltime

#SBATCH --mem 50G # 50G per GPU

#SBATCH --cpus-per-task 4 # 4 CPUs per GPU

#SBATCH --output %x.log #Location of output log

#SBATCH --gres gpu:1 # Number of GPUs to use (max 2)

#SBATCH --partition gpu # Must be run on GPU partition.

module load MATLAB/2018b

module load CUDA # Drivers for using GPU

#Job run

matlab -nodisplay -r "gpuDeviceCount()"

## Note

One GPU hour is costed the same as 56 CPU hours. The GPUs are a powerful resource and should only be used if you expect

significantspeedup.

# Adding Support Packages

If you have X-11 set up you can install additional package through the GUI. You can also install manually if you already have the files by copying them into your Support Package root directory..

Support packages are usually saved in your home directory, you can see the path using the MATLAB command `matlabshared.supportpkg.getSupportPackageRoot`

if it is unset, you can specify it with ` matlabshared.supportpkg.setSupportPackageRoot("<path>")`

# Improving performance with mexing

Like other scripting languages, MATLAB code will generally run slower than compiled code since every MATLAB instruction needs to be parsed and interpreted. Instructions inside large MATLAB loops are often performance hotspots due to the interpreter's overhead, which consumes CPU time at every iteration.

Fortunately MATLAB lets programmers extend their scripts with C/C++ or Fortran, which is referred to as mexing.

more info about compiling software on NeSI here.

## Writing mex functions

This involves the following steps (using C++ as an example):

- Focus on a loop to extend, preferably a nested set of loops.
- Identify the input and output variables of the section of code to extend.
- Write C++ code. The name of the C++ file should match the name of the function to call from MATLAB, e.g.
`myFunction.cpp`

for a function named`myFunction`

. - Compile the extension using the MATLAB command
`mex myFunction.cpp`

At the minimum, the C++ extension should contain:

#include <mex.h>

#include <matrix.h>

void mexFunction(int nlhs, mxArray *plhs[],

int nrhs, const mxArray *prhs[]) {

// implementation goes here

}

Note that the above function should always be called `mexFunction`

and its signature be `int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]`

. Here, `nlhs`

and `nrhs`

refer to the number of output and input arguments respectively. Access each dummy argument with index `i = 0 ... nlhs-1`

and `j = 0 ... nrhs-1`

respectively. Regardless of the type of argument, whether it is a number, a matrix or an object, its type is `mxArray`

. Often you will need to cast the argument into a corresponding C++ type, e.g.

// cast as a double, note the asterisk in front of mxGetPr

double x = (double) *mxGetPr(prhs[0]);

or

// cast as an array of doubles

double* arr = (double*) mxGetPr(prhs[0]);

Use `mxCreateDoubleMatrix`

and `mxCreateDoubleScalar`

to create a matrix and a number, respectively. For example:

// function returns [plhs[0], plhs[1]]

plhs[0] = mxCreateDoubleMatrix(3, 2, mxREAL); // 3 by 2 matrix

plhs[1] = mxCreateDoubleScalar(2); // number

All numbers are doubles. Use flat array indexing `a[i + n*j - 1]`

in C++ to access elements of a MATLAB matrix `a(i, j)`

of size `n x m`

.

MATLAB will take care of destroying matrices and other object so you should feel free to create objects inside C++ code (required for functions that have return values).

Some mex function source code examples can be found in the table here.

## Compilation

MATLAB supports the following compilers.

C++ | up to GCC 6.3.x |

C | up to GCC 6.3.x |

FORTRAN | up to GNU gfortran 6.3.x |

The most up to date compilers supported by MATLAB can be loaded on NeSI using `module load gimkl/2017a`

If no GCC module is loaded, the default system version of these compilers will be used.

Further configuration can be done within MATLAB using the command `mex -setup`

`mex <file_name>`

will then compile the mex function.

Default compiler flags can be overwritten with by setting the appropriate environment variables. The COMPFLAGS variable is ignored as it is Windows specific.

C++ | `CXXFLAGS` |

C | `CFLAGS` |

FORTRAN | `FFLAGS` |

Linker | `LDFLAGS` |

For example, adding OpenMP flags for a fortran compile:

mex FFLAGS='$FFLAGS -fopenmp' LDFLAGS='$LDFLAGS -fopenmp' <file_name>