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(Using the App)
(Example 1: basic use)
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The matlab script ''test.m'' has to be in the current directory. Control will be returned immediately after executing the matlabsubmit command. To check the run status or kill a job, use the respective batch scheduler commands (e.g. '''bjobs''' and '''bkill''' on ada). matlabsubmit will create a sub directory named '''MatlabSubmitLOG<N>''' (where '''N''' is the matlabsubmit ID). In this directory matlabsubmit will store all its relevant files; the generated batch script, matlab driver, redirected output and error, and a copy of the workspace (after the job is done). A listing of this directory will show the following files:  
+
The matlab script ''test.m'' has to be in the current directory. Control will be returned immediately after executing the matlabsubmit command. To check the run status or kill a job, use the respective batch scheduler commands (e.g. '''bjobs''' and '''bkill''' on ada). matlabsubmit will create a sub directory named '''MatlabSubmitLOG<N>''' (where '''N''' is the matlabsubmit ID). In this directory matlabsubmit will store all its relevant files; the generated batch script, matlab driver, and redirected output and error. A listing of this directory will show the following files:  
  
 
* '''lsf.err''' redirected error  
 
* '''lsf.err''' redirected error  
Line 200: Line 200:
 
* '''matlabsubmit_wrapper.m''' Matlab code that sets #threads and calls user function
 
* '''matlabsubmit_wrapper.m''' Matlab code that sets #threads and calls user function
 
* '''submission_script''' the generated LSF batch script
 
* '''submission_script''' the generated LSF batch script
* '''workspace.mat''' a copy of the matlab workspace (after execution has finished)
 
  
 
===Options with matlabsubmit===
 
===Options with matlabsubmit===

Revision as of 16:37, 30 July 2019

Running Matlab interactively on login nodes

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.

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/R2017a

This will setup the environment for Matlab version R2017a. 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, it is possible that using 8 thread might negatively affect runtime.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 (parallel) Matlab Scripts on HPRC compute nodes

When your Matlab script needs more resources than are allowed during an interactive session (e.g. cpu time, number of cores) you are not allowed to run it on a login node. In this section, we will introduce a number of options to run your Matlab scripts on HPRC compute nodes.

Run Matlab Scripts Remotely Using the HPRC Matlab App

HPRC developed a toolbox to run your Matlab script directly on HPRC (ada and terra) compute nodes from your own laptop and/or desktop. The toolbox contains the infrastructure to submit jobs from the Matlab command window as well as an App that can be used to submit jobs

Installing the toolbox

You can download the app here. After downloading install it from within the Matlab GUI by double-clicking on the file (or right-click and select install). After installing, the toolbox will show up as HPRC toolbox and the App will show up under the APPS tab under the name TAMU HPRC.

Using the App

You can start the HPRC app from the APPS tab. Alternatively, you can start the App by typing HPRC on the Matlab command line. The App window will popup and will look like:


Hprcapp.png


  • Click on the Browse button to select the MATLAB script you want to run. It will open a file selection dialog. You can also type the name of the script directly. NOTE: the file does not have to be in the current directory.
  • Select the cluster where you want to run (terra or ada) and provide your username (should be your TAMU netid).
  • In case your MATLAB script uses GPU commands/functions, check the Code Uses GPU box
  • Click on the Attach Input Files button to include any input files your script needs. It will open a file selection window where you can (multi) select files to include.


In addition to selecting the script name and the cluster/user there are many other options you can specify in the app (e.g. #workers in case you are running a parallel script, expected walltime needed, amount of memory needed, etc). For a detailed description of all the options, click here


To submit the script click on the SUBMIT button. A new window will popup that will show information about the job submission process.

NOTE: The first time you submit a script You will be asked to select a local directory where MATLAB will store Job information (a directory selection dialog will popup)

Retrieving results

Once the script has been successfully submitted a variable named myjob of MATLAB type Job will be copied to the workspace. You can use this variable to retrieve information about the job and retrieve varialbes. For example:

  • myjob.State will show the current status of the job. This can be queued , running, or finished
  • myjob.diary will display all the redirected screen output from your Matlab run.
  • myjob.load will load all the variables from your Matlab run into the current workspace

In addition, you can also get the Job information through the Parallel Job monitor (click Parallel --> Monitor Jobs). Use TAMUREMOTE cluster profile to see the corresponding jobs.

If you have any save commands in your script, the files will be saved in remote directory /scratch/user/${USER}/MatlabJobs/WORKDIR/. These files will not be copied back to your desktop/laptop.

Considerations for Using the App

The app can be used to run any general serial and/or parallel script. It's especially recommended for serial/multithreaded runs, MATLAB scripts that use GPU code, need a long time to run and/or need a large amount of memory. For license considerations, the app is most suitable when either a limited number of workers are requested or more than 20 workers (on ada, 28 on terra). The reason for this is that every additional worker requires an additional MDCS license token. If you run your code directly on the cluster and all workers can be started on a single node only a single license token is required)

In addition, consider the following:

  • input data (and script) needs to be transferred from the local host to the cluster. If data is large, it might take some time to transfer.
  • scripts and input data will be stored in the local temp dir, which has limited capacity. If input data is too large it might not fit in the temp dir
  • generated variables will be transferred back to the local host. If data is large it might take some to transfer

Submit Matlab Scripts Remotely or Locally From the Matlab Command Line

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

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. matlabsubmit will also generate boilerplate Matlab code to set up the environment (e.g. st 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
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.

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 (you only need to do this once). This can be done using by calling the following Matlab function.

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

To see all options for matlabsubmit type:

[ netID@cluster ~]$ matlabsubmit -h

Example 1: basic use

The following example shows the simplest use of matlabsubmit. It will execute matlab script test.m using default values for batch resources and Matlab resources. matlabsubmit will also print some useful information to the screen. As can be seen in the example, it will show the Matlab resources requested (e.g. #threads, #workers), the submit command that will be used to submit the job, the batch scheduler JobID, and the location of output generated by Matlab and the batch scheduler.

-bash-4.1$ matlabsubmit test.m

===============================================
Running Matlab script with following parameters
-----------------------------------------------
Script     : test.m
Workers    : 0
Nodes      : 1
Mem/proc   : 2500
#threads   : 8
===============================================

bsub  -e MatlabSubmitLOG1/lsf.err -o MatlabSubmitLOG1/lsf.out  
      -L /bin/bash -n 8 -R span[ptile=8] -W 02:00 -M 2500 
      -R rusage[mem=2500]      
      -J test1 MatlabSubmitLOG1/submission_script

Verifying job submission parameters...
Verifying project account...
     Account to charge:   082839397478For 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. 

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 (you only need to do this once). This can be done using by calling the following Matlab function.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. 

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 (you only need to do this once). This can be done using by calling the following Matlab function.
         Balance (SUs):     81535.6542
         SUs to charge:        16.0000
Job <2847580> is submitted to default queue <sn_regular>.

-----------------------------------------------
matlabsubmit ID        : 1
matlab output file     : MatlabSubmitLOG1/matlab.log
LSF/matlab output file : MatlabSubmitLOG1/lsf.out
LSF/matlab error file  : MatlabSubmitLOG1/lsf.err
-bash-4.1$


The matlab script test.m has to be in the current directory. Control will be returned immediately after executing the matlabsubmit command. To check the run status or kill a job, use the respective batch scheduler commands (e.g. bjobs and bkill on ada). matlabsubmit will create a sub directory named MatlabSubmitLOG<N> (where N is the matlabsubmit ID). In this directory matlabsubmit will store all its relevant files; the generated batch script, matlab driver, and redirected output and error. A listing of this directory will show the following files:

  • lsf.err redirected error
  • lsf.out redirected output (both LSF and Matlab)
  • matlab.log redirected Matlab screen output
  • matlabsubmit_wrapper.m Matlab code that sets #threads and calls user function
  • submission_script the generated LSF batch script

Options with matlabsubmit

The example above showed the most simple case of using matlabsubmit. No options where specified and matlabsubmit used default values for requested resources. However, matlabsubmit provides a number of options to set batch resources (e.g. walltime, memory) as well as matlab related options (e.g. number of threads to use, number of workers, etc). To see all the available options you can use the "-h" option. See below for the output of "matlabsubmit -h":


-bash-4.1$ matlabsubmit -h
/software/hprc/Matlab/bin/matlabsubmit: option requires an argument -- h
Usage: /software/hprc/Matlab/bin/matlabsubmit [options] SCRIPTNAME

This tools automates the process of running matlab codes on the compute nodes.

OPTIONS:
  -h Shows this message
  -m set the amount of requested memory in MEGA bytes(e.g. -m 20000)
  -t sets the walltime; form hh:mm Cluster profiles let you define certain properties for your cluster(e.g. -t 03:27)
  -w sets the number of ADDITIONAL workers
  -g indicates script needs GPU  (no value needed)
  -b sets the billing account to use 
  -s set number of threads for multithreading (default: 8 ( 1  when -w > 0)
  -p set number of workers per node
  -f run function call instead of script
  -x add explicit batch scheduler option
  
DEFAULT VALUES:
  memory   : 2500 per core 
  time     : 02:00
  workers  : 0
  gpu      : no gpu 
  threading: on, 8 threads
We will discuss briefly some of the more common parallel matlab concepts. For more detailed information about these constructs, as well as additional parallel constructs consult the Parallel Computing Toolbox User Guide.


-bash-4.1$

For example, the command matlabsubmit -t "03:27" -m 17000 -s 20 myscript.m will request 17gb of memory and 3 hours and 27 minutes of computing time. It will also set the number of computational threads in Matlab to 20 and execute the Matlab script myscript.m.

NOTE when using the -f flag to execute a function instead of a script, the function call must be enclosed with double quotes when it contains parentheses. For example: matlabsubmit -f "myfunc(21)"

Example 2: Utilizing Matlab workers (single node)

To utilize additional workers used by Matlab's parallel features such as parfor,spmd, and distributed matlabsubmit provides the option to specify the number of workers. This is done using the -w <N> flag (where <N> represents the number of workers). The following example shows a simple case of using additional workers; in this case 8 workers


-bash-4.1$ matlabsubmit -w 8 test.m
===============================================
Running Matlab script with following parameters
-----------------------------------------------
Script     : test.m
Workers    : 8
Nodes      : 1
Mem/proc   : 2500
#threads   : 1
===============================================

bsub  -e MatlabSubmitLOG5/lsf.err -o MatlabSubmitLOG5/lsf.out  
      -L /bin/bash -n 9 -R span[ptile=9] -W 02:00 -M 2500 
      -R rusage[mem=2500] 
      -J test5 MatlabSubmitLOG5/submission_script

Verifying job submission parameters...
Verifying project account...
     Account to charge:   082839397478
         Balance (SUs):     80533.2098
         SUs to charge:        18.0000
Job <2901543> is submitted to default queue <sn_regular>.

-----------------------------------------------
matlabsubmit ID        : 5
matlab output file     : MatlabSubmitLOG5/matlab.log
LSF/matlab output file : MatlabSubmitLOG5/lsf.out
LSF/matlab error file  : MatlabSubmitLOG5/lsf.err

-bash-4.1$

In this example, matlabsubmit will first execute matlab code to create a parpool with 8 workers (using the local profile). As can be seen in the output, in this case, matlabsubmit requests 9 cores: 1 core for the client and 8 cores for the workers. The only exception is when the user requests 20 workers. In that case, matlabsubmit will request 20 cores.


Example 3: Utilizing Matlab workers (multi node)

matlabsubmit provides excellent options for Matlab runs that need more than 20 workers (maximum for single node) and/or when the Matlab workers need to be distributed among multiple nodes. Reasons for distributing workers among different nodes include: need to use certain resources such as gpu on multiple nodes, enable multi threading on every worker, and use the available memory on multiple nodes. The following example shows how to run a matlab simulation that utilizes 24 workers, where every node will run 4 workers (i.e. the workers will be distributed among 24/4 = 6 nodes).

-bash-4.1$ matlabsubmit -w 24 -p 4 test.m
===============================================
Running Matlab script with following parameters
-----------------------------------------------
Script     : test.m
Workers    : 24
Nodes      : 6
Mem/proc   : 2500
#threads   : 1
===============================================

... starting matlab batch. This might take some time. 
See MatlabSubmitLOG8/matlab-batch-commands.log
...Starting Matlab from host: login4
MATLAB is selecting SOFTWARE OPENGL rendering.

                                           < M A T L A B (R) >
                                 Copyright 1984-2016 The MathWorks, Inc.
                                 R2016a (9.0.0.341360) 64-bit (glnxa64)
                                            February 11, 2016

 
To get started, type one of these: helpwin, helpdesk, or demo.
For product information, visit www.mathworks.com.
 
... Interactive Matlab session, multi threading reduced to 4

	Academic License


commandToRun =

bsub -L /bin/bash -J Job1 -o '/general/home/pennings/Job1/Job1.log' -n 25 -M 2500 
     -R rusage[mem=2500] -R "span[ptile=4]" -W 02:00       
     "source /general/home/pennings/Job1/mdce_envvars ;
     /general/software/x86_64/tamusc/Matlab/toolbox/tamu/profiles/lsfgeneric/communicatingJobWrapper.sh"


job = 

 Job

    Properties: 

                   ID: 1
                 Type: pool
             Username: pennings
                State: running
           SubmitTime: Mon Aug 01 12:15:15 CDT 2016
            StartTime: 
     Running Duration: 0 days 0h 0m 0s
      NumWorkersRange: [25 25]

      AutoAttachFiles: true
  Auto Attached Files: /general/home/pennings/MatlabSubmitLOG8/matlabsubmit_wrapper.m
                       /general/home/pennings/test.m
        AttachedFiles: {}
      AdditionalPaths: {}

    Associated Tasks: 

       Number Pending: 25
       Number Running: 0
      Number Finished: 0
    Task ID of Errors: []
  Task ID of Warnings: []




-----------------------------------------------
matlabsubmit JOBID            : 8
batch  output file (client)   : Job1/Task1.diary.txt
batch  output files (workers) : Job1/Task[2-25].diary.txt
Done

-bash-4.1$

As can be seen the output is very different from the previous examples. When a job uses multiple nodes the approach matlabsubmit uses is a bit different. matlabsubmit will start a regular interactive matlab session and from within it will run the Matlab batch command using the TAMUG cluster profile. It will then exit Matlab while the Matlab script is executed on the compute nodes.

The contents of the MatlabSubmitLOG directory are also slightly different. A listing will show the following files:

  • matlab-batch-commands.log screen output from Matlab
  • matlabsubmit_driver.m Matlab code that sets up the cluster profile and calls Matlab batch
  • matlabsubmit_wrapper.m Matlab code that sets #threads and calls user function
  • submission_script The actual command to start Matlab

In addition to the MatlabSubmitLOG directory created by matlabsubmit, Matlab will also create a directory named Job<N> used by the cluster profile to store meta data, log files, and screen output. The *.diary.txt text files will show screen output for the client and all the workers.

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);