MATLAB

MATLAB is a technical computing environment for high-performance numeric computation and visualization. MATLAB integrates numerical analysis, matrix computation, signal processing, and graphics in an easy-to-use environment where problems and solutions are expressed just as they are written mathematically--without traditional programming.

Availability and Restrictions

Versions

MATLAB is available on Pitzer and Owens Clusters. The versions currently available at OSC are:

Version Owens Pitzer Notes
r2015b X    
r2016b X    
r2017a X    
r2018a X X  
r2018b X X  
r2019a   X  
r2019b X X  
r2020a X* X*  
r2021b X X  
r2022a X X  
* Current default version

You can use module spider matlab to view available modules for a given machine. Feel free to contact OSC Help if you need other versions for your work.

Access: Academic Users Only (non-commercial, non-government)

Any academic users at OSC can use Matlab. All users must be added to the license server before using MATLAB. Please contact OSC Help to be granted access.

Publisher/Vendor/Repository and License Type

MathWorks, Commercial (University site license)

Usage

Usage on Owens

Set-up

To load the default version of MATLAB module, use  module load matlab . For a list of all available MATLAB versions and the format expected, type:  module spider matlab . To select a particular software version, use   module load matlab/version . For example, use  module load matlab/r2015b  to load MATLAB version r2015b. 

Running MATLAB

The following command will start an interactive, command line version of MATLAB:

matlab -nodisplay 
If you are able to use X-11 forwarding and have enabled it in your SSH client software preferences, you can run MATLAB using the GUI by typing the command  matlab . For more information about the matlab command usage, type  matlab –h  for a complete list of command line options.

The commands listed above will run MATLAB on the login node you are connected to. As the login node is a shared resource, running scripts that require significant computational resources will impact the usability of the cluster for others. As such, you should not use interactive MATLAB sessions on the login node for any significant computation. If your MATLAB script requires significant time, CPU power, or memory, you should run your code via the batch system.

Batch Usage

When you log into owens.osc.edu you are actually logged into a Linux box referred to as the login node. To gain access to the multiple processors in the computing environment, you must submit your job to the batch system for execution. Batch jobs can request multiple nodes/cores and compute time up to the limits of the OSC systems. Refer to Queues and Reservations and Batch Limit Rules for more info. 

Interactive Batch Session
For an interactive batch session using the command line version of MATLAB, one can run the following command:
sinteractive -A <project-account> -N 1 -n 28 -t 00:20:00

which requests one whole node with 28 cores ( -N 1 -n 28 ), for a walltime of 20 minutes ( -t 00:20:00 ). Here you can run MATLAB interactively by loading the MATLAB module and running MATLAB with the options of your choice as described above. You may adjust the numbers per your need.

Usage on Pitzer

Set-up

To load the default version of MATLAB module, use module load matlab.

Running MATLAB

The following command will start an interactive, command line version of MATLAB:

matlab -nodisplay 
If you are able to use X-11 forwarding and have enabled it in your SSH client software preferences, you can run MATLAB using the GUI by typing the command  matlab. For more information about the matlab command usage, type  matlab –h for a complete list of command line options.

The commands listed above will run MATLAB on the login node you are connected to. As the login node is a shared resource, running scripts that require significant computational resources will impact the usability of the cluster for others. As such, you should not use interactive MATLAB sessions on the login node for any significant computation. If your MATLAB script requires significant time, CPU power, or memory, you should run your code via the batch system.

Batch Usage

When you log into pitzer.osc.edu you are actually logged into a Linux box referred to as the login node. To gain access to the multiple processors in the computing environment, you must submit your job to the batch system for execution. Batch jobs can request multiple nodes/cores and compute time up to the limits of the OSC systems. Refer to Queues and Reservations and Batch Limit Rules for more info. 

Interactive Batch Session
For an interactive batch session using the command line version of MATLAB, one can run the following command:
sinteractive -A <project-account> -N 1 -n 40 -t 00:20:00

which requests one whole node with 40 cores ( -N 1 -n 40), for a walltime of 20 minutes ( -t 00:20:00 ). Here you can run MATLAB interactively by loading the MATLAB module and running MATLAB with the options of your choice as described above. You may adjust the numbers per your need.

Parallel Processing in MATLAB

MATLAB supports implicit multithreading on a single node, as well as MPI accross multiple nodes via MatLab Parallel Server's built-in implementation of MPI.  

Multithreading

Multithreading allows some functions in MATLAB to distribute the work load between cores of the node that your job is running on. By default, all of the current versions of MATLAB available on the OSC clusters have multithreading enabled. 

The system will run as many threads as there are cores on the nodes requested.

Multithreading increases the speed of some linear algebra routines, but if you would like to disable multithreading you may include the option " -singleCompThread" when running MATLAB. An example is given below:

#!/bin/bash
#SBATCH --job-name disable_multithreading
#SBATCH --time=00:10:00
#SBATCH --nodes=1 --ntasks-per-node=40
#SBATCH --account=<project-account>

module load matlab
matlab -singleCompThread -nodisplay -nodesktop < hello.m
# end of example file

Single node parallelization with 'parpool'

To parallelize MatLab processes across a single node, we will write a MatLab script that uses "parcluster" and "parpool" to setup a pool of workers.  Here is an example "Hello World" code for illustration:

% specify timezone to prevent warnings about timezone ambiguity

setenv('TZ', 'America/New_York')

% open the local cluster profile for a single node job
  p = parcluster('local');

  % open the parallel pool, recording the time it takes
  tic;
  parpool(p, 40); % open the pool using 40 workers
  fprintf('Opening the parallel pool took %g seconds.\n', toc)

  % "single program multiple data"
  spmd
    fprintf('Worker %d says Hello World!\n', labindex)
  end

  delete(gcp); % close the parallel pool
  exit

Since we will only be using a single  node, we will use the "local" cluster profile and set the parpool size to be equal to the number of cores on the compute node we'll be running on.  

Let's save this MatLab script as "helloworld.m" and then write a Slurm batch script to execute it:

#!/usr/bin/env bash

#SBATCH -N 1
#SBATCH --ntasks-per-node=40
#SBATCH --job-name=matlab_helloworld
#SBATCH --output=MatLabHello40.log

# load a MatLab module
module load matlab/r2020a

# create a variable for the basename of the script to be run
BASE_MFILE_NAME=helloworld

# execute code without a GUI
cd $SLURM_SUBMIT_DIR
matlab -nodisplay -r $BASE_MFILE_NAME

In the above batch script, we load a MatLab module, create a variable to specify the basename of Hello World script, and then execute the script with the "-nodisplay" flag to prevent the GUI from trying to load. The script uses 40 workers since Pitzer nodes have 40 or 48 cores.

Next, let's save this file as "helloworld.slurm" and submit the job script to the Slurm scheduler with "sbatch helloworld.slurm."

The output will be found in the log file "MatLabHello40.log":

                          < M A T L A B (R) >
                  Copyright 1984-2022 The MathWorks, Inc.
             R2020a Update 5 (9.10.0.1739362) 64-bit (glnxa64)
                               April 18, 2022

To get started, type doc.
For product information, visit www.mathworks.com.

Starting parallel pool (parpool) using the 'local' profile ...
Connected to the parallel pool (number of workers: 40).
Opening the parallel pool took 24.7213 seconds.
Lab  1:
  Worker 1 says Hello World!
....
Lab 40:
  Worker 40 says Hello World!
Parallel pool using the 'local' profile is shutting down.

We can see from the above that there is a "Hello World" statement from each of the 40 workers.

Multi-Node Processing

It's also possible to use multiple nodes to run MatLab code  via MatLab Parallel Server's built-in implementation of MPI, but this process requires a bit of intial setup:

1. First we need to load the matlab module and run the matlab executable in GUI mode:

module load matlab/r2020a
matlab

2. Next, within the matlab GUI, navigate to HOME->Environment->Parallel->Create and Manage Clusters:

3. Next, we create a new cluster profile by navigating to "Add Cluster Profile" and choosing the Slurm scheduler:

4. Finally, we edit the cluster profile to suite the needs of the job we will be running. Most of the settings can be left as default, though the following should be set:
Resource arguments for job submission  - these are the SBATCH flags used to control the job's behavior and resources utilized.  At a minimum, the "--account" flag should be set to a valid account name.

Screenshot 2022-04-18 120321.png

 

Finally, after editing the Slurm cluster profile to specify the resources neeed for your job, we can launch the job with by executing a MatLab script at the command line (note that this can also be done within the MatLab GUI):

% specify timezone to prevent warnings about timezone ambiguity

setenv('TZ', 'America/New_York')

% open the desired Slurm cluster profile
  p = parcluster('SlurmProfile1');

  % open the parallel pool, recording the time it takes
  tic;
  parpool(p, 80); % open the pool using 80 workers which will be spread across 2 nodes
  fprintf('Opening the parallel pool took %g seconds.\n', toc)

  % "single program multiple data"
  spmd
    fprintf('Worker %d says Hello World!\n', labindex)
  end

  delete(gcp); % close the parallel pool
  exit

This time, we've specified the 'SlurmProfile1' profile to the parcluster command. In addition, since the cluster profile is set to use 2 nodes on Pitzer, when we open the parallel pool we set the worker count to 80 workers. 

Let's save this file as 'helloworld_slurm.m' and execute it:

matlab -nodisplay -r helloworld_slurm > helloworld_slurm.log

This will use MatLab's Slurm cluster profile to submit the code to the scheduler. The job may sit in the queue if the cluster is busy, but once it has run, we see a Hello World statement printed from 80 different workers printed in the log file:

...
Lab 45:
  Worker 45 says Hello World!
Lab 46:
  Worker 46 says Hello World!
Lab 47:
  Worker 47 says Hello World!
...

Multi-node Slurm MatLab jobs can also be submitted using a specific Slurm MatLab plugin, which may offer more scriptable flexibility.  To learn more about that option, please see here.

Concurrent jobs on OSC clusters

When you run multiple jobs concurrently, each job will try to access your preference files at the same time. It may create a race condition issue and may cause a negative impact on the system and the failure of your jobs. In order to avoid this issue, please add the following in your job script:

export MATLAB_PREFDIR=$TMPDIR

It will reset the preference directory to the local temporary directory, $TMPDIR. If you wish to start your Matlab job with the preference files you already have, add the following before you change MATLAB_PREFDIR.

cp -a ~/.matlab/{matlab version}/* $TMPDIR/

If you use matlab/r2020a, your matlab version is "R2020a".

MATLAB with a GPU

A GPU can be utilized for MATLAB. You can acquire a GPU by

#SBATCH --gpus-per-node=1

for Owens, or Pitzer. For more detail, please read here.

You can check the GPU assigned to you using:

gpuDeviceCount  # show how many GPUs you have
gpuDevice       # show the details of the GPU

You can replace an array to gpuArray, for example:

A = gpuArray(A)

where A is a regular MATLAB array. This transfers the array data to the GPU memory. Then, you can use the gpuArray variable in GPU supported built-in functions. You can find the full list of GPU supported built-in functions from here. For more information about GPU programming for MATLAB, please read "GPU Computing" from Mathworks.

Toolboxes and Features

OSC's current licenses support the following MATLAB toolboxes and features (please contact OSC Help for license-specific questions):

MATLAB
Simulink
5G Toolbox
AUTOSAR Blockset
Aerospace Blockset
Aerospace Toolbox
Antenna Toolbox
Audio Toolbox
Automated Driving Toolbox
Bioinformatics Toolbox
Communications Toolbox
Computer Vision Toolbox
Control System Toolbox
Curve Fitting Toolbox
DDS Blockset
DSP System Toolbox
Data Acquisition Toolbox
Database Toolbox
Datafeed Toolbox
Deep Learning HDL Toolbox
Deep Learning Toolbox
Econometrics Toolbox
Embedded Coder
Filter Design HDL Coder
Financial Instruments Toolbox
Financial Toolbox
Fixed-Point Designer
Fuzzy Logic Toolbox
GPU Coder
Global Optimization Toolbox
HDL Coder
HDL Verifier
Image Acquisition Toolbox
Image Processing Toolbox
Instrument Control Toolbox
LTE Toolbox
Lidar Toolbox
MATLAB Coder
MATLAB Compiler SDK
MATLAB Compiler
MATLAB Report Generator
Mapping Toolbox
Mixed-Signal Blockset
Model Predictive Control Toolbox
Model-Based Calibration Toolbox
Motor Control Blockset
Navigation Toolbox
OPC Toolbox
Optimization Toolbox
Parallel Computing Toolbox
Partial Differential Equation Toolbox
Phased Array System Toolbox
Powertrain Blockset
Predictive Maintenance Toolbox
RF Blockset
RF PCB Toolbox
RF Toolbox
ROS Toolbox
Radar Toolbox
Reinforcement Learning Toolbox
Risk Management Toolbox
Robotics System Toolbox
Robust Control Toolbox
Satellite Communications Toolbox
Sensor Fusion and Tracking Toolbox
SerDes Toolbox
Signal Integrity Toolbox
Signal Processing Toolbox
SimBiology
SimEvents
Simscape Driveline
Simscape Electrical
Simscape Fluids
Simscape Multibody
Simscape
Simulink 3D Animation
Simulink Check
Simulink Code Inspector
Simulink Coder
Simulink Compiler
Simulink Control Design
Simulink Coverage
Simulink Design Optimization
Simulink Design Verifier
Simulink Desktop Real-Time
Simulink PLC Coder
Simulink Real-Time
Simulink Report Generator
Simulink Requirements
Simulink Test
SoC Blockset
Spreadsheet Link
Stateflow
Statistics and Machine Learning Toolbox
Symbolic Math Toolbox
System Composer
System Identification Toolbox
Text Analytics Toolbox
UAV Toolbox
Vehicle Dynamics Blockset
Vehicle Network Toolbox
Vision HDL Toolbox
WLAN Toolbox
Wavelet Toolbox
Wireless HDL Toolbox

See this page if you need to install additional toolbox by yourself. 

Further Reading

Official PDF documentation can be obtained from the MathWorks Website

References

1. Using MatLab's Parallel Server for multi-core and multi-node jobs

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