Parallel Computing Toolbox™ provides the Generic cluster type for submitting MATLAB® jobs to a cluster running a third-party scheduler.
The Generic cluster type uses a set of plugin scripts to define how your machine communicates with your scheduler.
You can customize the plugin scripts to configure how MATLAB interacts with the scheduler to best suit your cluster setup and support custom submission options.
This repository contains MATLAB code files and shell scripts that you can use to submit jobs from a MATLAB or Simulink session running on Windows®, Linux®, or macOS operating systems to a Slurm® scheduler running on Linux.
- MATLAB and Parallel Computing Toolbox, R2017a or newer, installed on your computer. Refer to the documentation for how to install MATLAB and toolboxes on your computer.
- MATLAB Parallel Server™ installed on the cluster. Refer to the documentation for how to install MATLAB Parallel Server on your cluster. The cluster administrator normally does this step.
- Slurm running on the cluster.
To download a zip archive of this repository, at the top of this repository page, select Code > Download ZIP. Alternatively, to clone this repository to your computer with Git software installed, enter this command at your system's command line:
git clone https://github.com/mathworks/matlab-parallel-slurm-plugin
You can execute a system command from the MATLAB command prompt by adding ! before the command.
Since version R2023a, MATLAB can discover clusters running third-party schedulers such as Slurm.
As a cluster admin, you can create a configuration file that describes how to configure the Parallel Computing Toolbox on the user's machine to submit MATLAB jobs to the cluster.
The cluster configuration file is a plain text file with the extension .conf containing key-value pairs that describe the cluster configuration information.
The MATLAB client will use the cluster configuration file to create a cluster profile for the user who discovers the cluster.
Therefore, users will not need to follow the instructions in the sections below.
You can find an example of a cluster configuration file in discover/example.conf.
For full details on how to make a cluster running a third-party scheduler discoverable, see the documentation for Configure for Third-Party Scheduler Cluster Discovery.
Create a cluster profile by using either the Cluster Profile Manager or the MATLAB Command Window.
To open the Cluster Profile Manager, on the Home tab, in the Environment section, select Parallel > Create and Manage Clusters.
In the Cluster Profile Manager, select Add Cluster Profile > Generic from the menu to create a new Generic cluster profile.
Alternatively, create a new Generic cluster object by entering this command in the MATLAB Command Window:
c = parallel.cluster.Generic;This table lists the properties that you must specify to configure the Generic cluster profile.
For a full list of cluster properties, see the documentation for parallel.Cluster.
| Property | Description |
|---|---|
JobStorageLocation |
Folder in which your machine stores job data. |
NumWorkers |
Number of workers your license allows. |
ClusterMatlabRoot |
Full path to the MATLAB install folder on the cluster. |
OperatingSystem |
Cluster operating system. |
HasSharedFilesystem |
Indication of whether you have a shared file system. Set this property to true if a disk location is accessible to your machine and the workers on the cluster. Set this property to false if you do not have a shared file system. |
PluginScriptsLocation |
Full path to the plugin script folder that contains this README. If using R2019a or earlier, this property is called IntegrationScriptsLocation. |
In the Cluster Profile Manager, set each property value. Alternatively, at the command line, set properties using dot notation:
c.JobStorageLocation = 'C:\MatlabJobs';At the command line, you can also set properties when you create the Generic cluster object by using name-value arguments:
c = parallel.cluster.Generic( ...
'JobStorageLocation', 'C:\MatlabJobs', ...
'NumWorkers', 20, ...
'ClusterMatlabRoot', '/usr/local/MATLAB/R2022a', ...
'OperatingSystem', 'unix', ...
'HasSharedFilesystem', true, ...
'PluginScriptsLocation', 'C:\MatlabSlurmPlugin\shared');To submit from a Windows machine to a Linux cluster, specify JobStorageLocation as a structure with the fields windows and unix.
The fields are the Windows and Unix paths corresponding to the folder in which your machine stores job data.
For example, if the folder \\organization\matlabjobs\jobstorage on Windows corresponds to /organization/matlabjobs/jobstorage on the Unix cluster:
struct('windows', '\\organization\matlabjobs\jobstorage', 'unix', '/organization/matlabjobs/jobstorage')If you have your M: drive mapped to \\organization\matlabjobs, set JobStorageLocation to:
struct('windows', 'M:\jobstorage', 'unix', '/organization/matlabjobs/jobstorage')You can use AdditionalProperties to modify the behaviour of the Generic cluster without editing the plugin scripts.
For a full list of the AdditionalProperties supported by the plugin scripts in this repository, see Customize Behavior of Sample Plugin Scripts.
By modifying the plugins, you can add support for your own custom AdditionalProperties.
To manage work on the cluster, MATLAB calls the Slurm command line utilities.
For example, the sbatch command to submit work and squeue to query the state of submitted jobs.
If your MATLAB session is running on a machine with the scheduler utilities available, the plugin scripts can call the utilities on the command line.
Scheduler utilities are typically available if your MATLAB session is running on the Slurm cluster to which you want to submit.
If MATLAB cannot directly access the scheduler utilities on the command line, the plugin scripts create an SSH session to the cluster and run scheduler commands over that connection.
To configure your cluster to submit scheduler commands via SSH, set the ClusterHost field of AdditionalProperties to the name of the cluster node to which MATLAB connects via SSH.
As MATLAB will run scheduler utilities such as sbatch and squeue, select the cluster head node or login node.
In the Cluster Profile Manager, add new AdditionalProperties by clicking Add under the table corresponding to AdditionalProperties.
In the Command Window, use dot notation to add new fields.
For example, if MATLAB should connect to 'slurm01.organization.com' to submit jobs, set:
c.AdditionalProperties.ClusterHost = 'slurm01.organization.com';Use this option to connect to a remote cluster to submit jobs from a MATLAB session on a Windows computer to a Linux Slurm cluster on the same network. Your Windows machine creates an SSH session to the cluster head node to access the Slurm utilities and uses a shared network folder to store job data files.
If your MATLAB session is running on a compute node of the cluster to which you want to submit work, you can use this option to create an SSH session back to the cluster head node and submit more jobs.
MATLAB uses files on disk to send tasks to the Parallel Server workers and fetch their results. This is most effective when the disk location is accessible to your machine and the workers on the cluster. Your computer can communicate with the workers by reading and writing to this shared file system.
If you do not have a shared file system, MATLAB uses SSH to submit commands to the scheduler and SFTP (SSH File Transfer Protocol) to copy job and task files between your computer and the cluster.
To configure your cluster to move files between the client and the cluster with SFTP, set the RemoteJobStorageLocation field of AdditionalProperties to a folder on the cluster that the workers can access.
Transferring large data files (for example, hundreds of MB) over the SFTP connection can add a noticeable overhead to job submission and fetching results. For optimal performance, use a shared file system if one is available. The workers require access to a shared file system, even if your computer cannot access it.
In the Cluster Profile Manager, click Done. Alternatively, in the Command Window, enter the command:
saveAsProfile(c, 'mySLURMCluster');Your cluster profile is now ready to use.
Cluster validation submits one job of each type to test whether the cluster profile is configured correctly. In the Cluster Profile Manager, click Validate. If you make a change to a cluster profile, run cluster validation to ensure your changes have introduced no errors. You do not need to validate the profile each time you use it or each time you start MATLAB.
Create a cluster object using your profile:
c = parcluster("mySLURMCluster")The batch command runs a MATLAB script or function on a worker on the cluster.
For more information about batch processing, see the documentation for batch.
% Create a job and submit it to the cluster.
job = batch( ...
c, ... % Cluster object created using parcluster
@sqrt, ... % Function or script to run
1, ... % Number of output arguments
{[64 100]}); % Input arguments
% Your MATLAB session is now available to do other work, such
% as create and submit more jobs to the cluster. You can also
% shut down your MATLAB session and come back later. The work
% continues running on the cluster. After you recreate the
% cluster object using parcluster, you can view existing jobs
% using the Jobs property of the cluster object.
% Wait for the job to complete. If the job is already complete,
% the wait function will return immediately.
wait(job);
% Retrieve the output arguments for each task. For this example,
% the output is a 1x1 cell array containing the vector [8 10].
results = fetchOutputs(job)A parallel pool (parpool) is a group of MATLAB workers on which you can interactively run work.
When you run the parpool command, MATLAB submits a special job to the cluster to start the workers.
Once the workers start, your MATLAB session connects to them.
Depending on the network configuration at your organization, including whether it is permissible to connect to a program running on a compute node, parpools may not be functional.
For more information about parpools, see the documentation for parpool.
% Open a parallel pool on the cluster. This command returns a
% pool object once the pool is opened.
pool = parpool(c);
% List the hosts on which the workers are running. For a small pool,
% all the workers are typically on the same machine. For a large
% pool, the workers are usually spread over multiple nodes.
future = parfevalOnAll(pool, @getenv, 1, 'HOST')
wait(future);
fetchOutputs(future)
% Output the numbers 1 to 10 in a parallel for loop. Unlike a
% regular for loop, the software does not execute iterations
% of the loop in order.
parfor idx = 1:10
disp(idx)
end
% Use the pool to calculate the first 500 magic squares.
parfor idx = 1:500
magicSquare{idx} = magic(idx);
endThe license is available in the license.txt file in this repository.
If you require assistance or have a request for additional features or capabilities, please contact MathWorks Technical Support.
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