# SW:Matlab

## Contents

# Running Matlab interactively

Matlab is accessible to all HPRC users within the terms of our license agreement. If you have particular concerns about whether specific usage falls within the TAMU HPRC license, please send an email to HPRC Helpdesk. You can start a Matlab session either directly on a login node or through our portal

## Running Matlab on a login node

To be able to use Matlab, the Matlab module needs to be loaded first. This can be done using the following command:

[ netID@cluster ~]$module load Matlab/R2019a

This will setup the environment for Matlab version R2019a. To see a list of all installed versions, use the following command:

[ netID@cluster ~]$module spider Matlab

**Note:** New versions of software become available periodically. Version numbers may change.

To start matlab, use the following command:

[ netID@cluster ~]$matlab

Depending on your X server settings, this will start either the Matlab GUI or the Matlab command-line interface. To start Matlab in command-line interface mode, use the following command with the appropriate flags:

[ netID@cluster ~]$matlab -nosplash -nodisplay

By default, Matlab will execute a large number of built-in operators and functions multi-threaded and will use as many threads (i.e. cores) as are available on the node. Since login nodes are shared among all users, HPRC restricts the number of computational threads to 8. This should suffice for most cases. Speedup achieved through multi-threading depends on many factors and in certain cases. To explicitly change the number of computational threads, use the following Matlab command:

>>feature('NumThreads',4);

This will set the number of computational threads to 4.

To completely disable multi-threading, use the -singleCompThread option when starting Matlab:

[ netID@cluster ~]$matlab -singleCompThread

## Usage on the Login Nodes

Please limit interactive processing to short, non-intensive usage. Use non-interactive batch jobs for resource-intensive and/or multiple-core processing. Users are requested to be **responsible** and **courteous to other users** when using software on the login nodes.

The most important processing limits here are:

**ONE HOUR**of**PROCESSING TIME**per login session.**EIGHT CORES**per login session on the same node or (cumulatively) across all login nodes.

**Anyone found violating the processing limits will have their processes killed without warning. Repeated violation of these limits will result in account suspension.**

**Note:** Your login session will disconnect after **one hour** of inactivity.

## Running Matlab through the hprc portal

HPRC provides a portal through which users can start an interactive Matlab GUI session inside a web browser. For more information how to use the portal see our HPRC OnDemand Portal section

# Running Matlab through the batch system

HPRC developed a tool named **matlabsubmit** to run Matlab simulations on the HPRC compute nodes without the need to create your own batch script and without the need to start a Matlab session. **matlabsubmit** will automatically generate a batch script with the correct requirements. In addition, **matlabsubmit** will also generate boilerplate Matlab code to set up the environment (e.g. the number of computational threads) and, if needed, will start a *parpool* using the correct Cluster Profile (*local* if all workers fit on a single node and a *TAMU* cluster profile otherwise)

To submit your Matlab script, use the following command:

[ netID@cluster ~]$ matlabsubmit myscript.m

In the above example, **matlabsubmit** will use all default values for runtime, memory requirements, the number of workers, etc. To specify resources, you can use the command-line options of **matlabsubmmit**. For example:

[ netID@cluster ~]$ matlabsubmit -t 07:00 -s 4 myscript.m

The above example will set the wall-time to 7 hours and Matlab will use 4 computational threads for its run ( **matlabsubmit** will request 4 cores). To see all options for **matlabsubmit** type:

[ netID@cluster ~]$ matlabsubmit -h

For parallel processing, Matlab uses Cluster profiles. A cluster profile acts as an interface between Matlab and the batch scheduler (e.g. LSF, SLURM) and lets you define certain properties of your cluster (e.g. how to submit jobs, submission parameters, job requirements, etc). Matlab will use the cluster profile to offload parallel (or sequential) Matlab code to one or more workers.

When executing, matlabsubmit will do the following:

- generate boiler plate Matlab code to setup the matlab environment (e.g. #threads, #workers)
- generate a batch script with all resources set correctly and the command to run matlab
- submit the generated batch script to the batch scheduler and return control back to the user

For detailed examples on using matlabsubmit see the examples section.

# Running (parallel) Matlab Scripts on HPRC compute nodes

**NOTE:** Due to the new 2-factor authentication mechanism, this method does not work at the moment. We will update this wiki page when this is fixed.

For detailed information how to submit Matlab codes remotely, click here

## Submit Matlab Scripts Remotely or Locally From the Matlab Command Line

**NOTE:** Due to the new 2-factor authentication mechanism, remote submission method does not work at the moment. We will update this wiki page when this is fixed.

Instead of using the App you can also call Matlab functions (developed by HPRC) directly to run your Matlab script on HPRC compute nodes. There are two steps involved in submitting your Matlab script:

- Define the properties for your Matlab script (e.g. #workers). HPRC created a class named
**TAMUClusterProperties**for this - Submit the Matlab script to run on HPRC compute nodes. HPRC created a function named
**tamu_run_batch**for this.

For example, suppose you have a script named *mysimulation.m*, you want to use 4 workers and estimate it will need less than 7 hours of computing time:

>> tp=TAMUClusterProperties(); >> tp.workers(4); >> tp.walltime('07:00'); >> myjob=tamu_run_batch(tp,'mysimulation.m');

**NOTE:** **TAMUClusterProperties** will use all default values for any of the properties that have not been set explicitly.

In case you want to submit your Matlab script remotely from your local Matlab GUI, you also have to specify the HPRC cluster name you want to run on and your username. For example, suppose you have a script that uses Matlab GPU functions and you want to run it on terra:

>> tp=TAMUClusterProperties(); >> tp.gpu(1); >> tp.hostname('terra.tamu.edu'); >> tp.user('<USERNAME>'); >> myjob=tamu_run_batch(tp,'mysimulation.m');

To see all available methods on objects of type **TAMUClusterProperties** you can use the Matlab **help** or **doc** functions: E.g.

>> help TAMUClusterProperties/doc

To see help page for **tamu_run_batch**, use:

>> help tamu_run_batch tamu_run_batch runs Matlab script on worker(s). j = TAMU_RUN_BATH(tp,'script') runs the script script.m on the worker(s) using the TAMUClusterProperties object tp. Returns j, a handle to the job object that runs the script.

**tamu_run_batch** returns a variable of type **Job**. See the section *"Retrieve results and information from Submitted Job"* how to get results and information from the submitted job.

## Submit Matlab Scripts Directly from HPRC Login Shell

# Using Matlab Parallel Toolbox on HPRC Resources

*THIS SECTION IS UNDER CONSTRUCTION*

In this section, we will discuss some common concepts from the Matlab Parallel Toolbox and the convenience functions HPRC created to utilize the Parallel toolbox. We will give a brief introduction into Matlab Cluster profiles, parallel pools, the parallel constructs *parfor* and *spmd* , and how to utilize GPUs using Matlab.

The central concept in most of the convenience functions is the **TAMUClusterProperties** class introduced in the *Submit Matlab Scripts Remotely or Locally From the Matlab Command Line* section above.

## Cluster Profiles

Cluster profiles define properties on where and how you want to do the parallel processing. There are two kinds of profiles.

- local profiles: parallel processing is limited to the same node the Matlab client is running.
- cluster profiles: parallel processing can span multiple nodes; profile interacts with batch scheduler (e.g. LSF on ada, SLURM on terra).

**NOTE:** we will not discuss *local profiles* any further here. Processing using a local profile is exactly the same as processing using cluster profiles.

### Importing Cluster Profile

For your convenience, HPRC already created a custom Cluster Profile. You can use this profile to define how many workers you want, how you want to distribute the workers over the nodes Before you can use this profile you need to import it first. This can be done using by calling the following Matlab function.

>>tamu_import_TAMU_clusterprofile()

This function imports the cluster profile and it creates a directory structure in your scratch directory where Matlab will store meta information during parallel processing. The default location is */scratch/$USER/MatlabJobs/TAMU* ( */scratch/$USER/MatlabJobs/TAMUREMOTE* for remote jobs)

**NOTE:** convenience function **tamu_import_TAMU_clusterprofile** is a wrapper around the Matlab function
parallel.importprofile

You only need to import the cluster profile once. However, the imported profile is just a skeleton. It doesn't contain information how many resources (e.g. #workers) you want to use for parallel processing. In the next section, we will discuss how to create a fully populated cluster object that can be used for parallel processing.

For more information about **tamu_import_TAMU_clusterprofile()** you can use the Matlab *help// and *doc* functions.*

### Retrieving fully populated Cluster Profile Object

To return a fully completed cluster object (i.e. with attached resource information) HPRC created the **tamu_set_profile_properties** convenience function. There are two steps to follow:

- define the properties using the TAMUClusterProperties class
- call
**tamu_set_profile_properties**using the created TAMUClusterProperties object.

For example, suppose you have Matlab code and want to use 4 workers for parallel processing.

>> tp=TAMUClusterProperties; >> tp.workers(4); >> clusterObject=tamu_set_profile_properties(tp);

Variable *clusterObject* is a fully populated cluster object that can be used for parallel processing.

**NOTE:** convenience function **tamu_set_profile_properties** is a wrapper around Matlab function
parcluster. It also uses HPRC convenience function **tamu_import_TAMU_clusterprofile** to check if the **TAMU** profile has been imported already.

## Starting a Parallel Pool

To start a parallel pool you can use the HPRC convenience function **tamu_parpool**. It takes as argument a **TAMUClustrerProperties** object that specifies all the resources that are requested.

The **parpool** functions enables the full functionality of the parallel language features (parfor and spmd, will be discussed below). A parpool creates a special job on a pool of workers, and connects the pool to the MATLAB client. For example:

mypool = parpool 4 : delete(mypool)

This code starts a worker pool using the default cluster profile, with 4 additional workers.

NOTE: only instructions within parfor and spmd blocks are executed on the workers. All other instructions are executed on the client.

NOTE: all variables declared inside the matlabpool block will be destroyed once the block is finished.

## Common Parallel constructs

### parfor

The concept of a parfor-loop is similar to the standard Matlab for-loop. The difference is that parfor partitions the iterations among the available workers to run in parallel. For example:

parfor i=1:1024 A(i)=sin((i/1024)*2*pi); end

This code will open a parallel pool with 2 workers using the default cluster profile and execute the loop in parallel.

For more information please visit the Matlab parfor page.

### spmd

spmd runs the same program on all workers concurrently. A typical use of spmd is when you need to run the same program on multiple sets of input. For example, Suppose you have 4 inputs named data1,data2,data3,data4 and you want run function myfun on all of them:

spmd (4) data = load(['data' num2str(labindex)]) myresult = myfun(data) end

NOTE: labindex is a Matlab variable and is set to the worker id, values range from 1 to number of workers.

Every worker will have its own version of variable myresult. To access these variables outside the spmd block you append {i} to the variable name, e.g. myresult{3} represents variable myresult from worker 3.

For more information please visit the Matlab spmd page.

## Using GPU

Normally all variables reside in the client workspace and matlab operations are executed on the client machine. However, Matlab also provides options to utilize available GPUs to run code faster. Running code on the gpu is actually very straightforward. Matlab provides GPU versions for many build-in operations. These operations are executed on the GPU automatically when the variables involved reside on the GPU. The results of these operations will also reside on the GPU. To see what functions can be run on the GPU type:

methods('gpuArray') This will show a list of all available functions that can be run on the GPU, as well as a list of available static functions to create data on the GPU directly (will be discussed later).

NOTE: There is significant overhead of executing code on the gpu because of memory transfers.

Another useful function is: gpuDevice This functions shows all the properties of the GPU. When this function is called from the client (or a node without a GPU) it will just print an error message.

To copy variables from the client workspace to the GPU, you can use the gpuArray command. For example:

carr = ones(1000); garr = gpuArray(carr);

will copy variable carr to the GPU wit name garr.

In the example above the 1000x1000 matrix needs to be copied from the client workspace to the GPU. There is a significant overhead involved in doing this.

To create the variables directly on the GPU, Matlab provides a number of convenience functions. For example:

garr=gpuArray.ones(1000)

This will create a 1000x1000 matrix directly on the GPU consisting of all ones.

To copy data back to the client workspace Matlab provides the gather operation.

carr2 = gather(garr)

This will copy the array garr on the GPU back to variable carr2 in the client workspace.

The next example performs a matrix multiplication on the client, a matrix multiplication on the GPU, and prints out elapsed times for both. The actual cpu-gpu matrix multiplication code can be written as:

ag = gpuArray.rand(1000); bg = ag*ag; c = gather(bg);