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
MATLAB is available on Oakley, Ruby, and Owens Clusters. The versions currently available at OSC are:
| Version | Oakley | Ruby | Owens | notes |
|---|---|---|---|---|
| 2013a | X | Default version on Oakley prior to 09/15/2015 | ||
| 2013b | X | |||
| 2014a | X | X | Default version on Ruby prior to 09/15/2015 | |
| 2014b | X | X | ||
| 2015a | X | X | ||
| 2015b | X | X | X | |
| 2016b | X* | X* | X* | |
| 2017a | X | X | X |
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 to Academic Users
All academic users must be added to the license server before using MATLAB. Please contact OSC Help to be granted access.
Another method to use MATLAB on OSC is to use the MATLAB Distributed Computing Server on Oakley via your local MATLAB client. In order to use this feature, you should have your own MATLAB license, including the Parallel Computing Toolbox. See HOW-TO: Configure the MATLAB Parallel Computing Toolbox.
Usage on Oakley
Set-up
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/r2014b to load MATLAB version 2014b. Running MATLAB
The following command will start an interactive, command line version of MATLAB:
matlab -nodisplayIf 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 oakley.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. Batch jobs run on the compute nodes of the system and not on the login node. It is desirable for big problems since more resources can be used.
Interactive Batch Session
For an interactive batch session using the command line version of MATLAB, one can run the following command:qsub -I -X -l nodes=1:ppn=12 -l walltime=00:20:00
which requests one whole node with 12 cores ( -l nodes=1:ppn=12 ), for a walltime of 20 minutes ( -l walltime=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. The –X flag enables X11 forwarding on the compute node, so you can use the MATLAB GUI if you choose.You may adjust the numbers per your need.
Non-interactive Batch Job (Serial Run)
In order to run MATLAB non-interactively via the batch system, you will require a batch submission script and a MATLAB M-file containing the script that will be run via the batch system. A batch script can be created and submitted for a serial or parallel run. You can create the batch script using any text editor you like in a working directory on the system of your choice. Below is an example batch submission script and a simple M-file. The batch script runs the M-file via the batch system.Example batch script ( job.txt ) for a serial run:
#PBS -N matlab_example #PBS -l walltime=00:10:00 #PBS -l nodes=1:ppn=12 #PBS -j oe module load matlab matlab -nodisplay -nodesktop < hello.m # end of example file
Example M-file, hello.m:
%Example M-file for Hello World disp 'Hello World' exit % end of example fileIn order to run
hello.m via the batch system, submit the job.txt file with the following command:
qsub job.txt
This will run hello.m via the batch system, and all output from the running of the script will be saved in the output.txt file.
Parallel Processing in MATLAB
MATLAB supports both implicit multiprocessing (multithreading) and explicit multiprocessing across multiple nodes.
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 use a number of threads equal to the number of cores you request. Therefore, if you request nodes=1:ppn=4, your job will only spawn four threads.
Multithreading increases the speed of some linear algebra routines, but if you would like to disable multithreading you may request nodes=1:ppn=1 and include the option -singleCompThread when running MATLAB. An example is given below:
#PBS -N disable_multithreading #PBS -l walltime=00:10:00 #PBS -l nodes=1:ppn=1 #PBS -j oe module load matlab matlab -singleCompThread -nodisplay -nodesktop < hello.m # end of example file
Parallel computing across multiple nodes
You can accomplish parallel processing using multiple nodes by using the Parallel Computing Toolbox in conjunction with the MATLAB Distributed Computing Server. For more information about configuration and usage, see HOW-TO: Configure the MATLAB Parallel Computing Toolbox.
Usage on Ruby
Set-up
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/r2014b to load MATLAB version 2014b. Running MATLAB
The following command will start an interactive, command line version of MATLAB:
matlab -nodisplayIf 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 ruby.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:qsub -I -X -l nodes=1:ppn=20 -l walltime=00:20:00
which requests one whole node with 20 cores ( -l nodes=1:ppn=20 ), for a walltime of 20 minutes ( -l walltime=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. The –X flag enables X11 forwarding on the compute node, so you can use the MATLAB GUI if you choose.You may adjust the numbers per your need.
Parallel Processing in MATLAB
MATLAB supports both implicit multiprocessing (multithreading) and explicit multiprocessing across multiple nodes.
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. For the normal nodes, this mean matlab will start 20 threads to run on the 20 cores of the node. This is due to the whole node scheduling policy of Ruby (see Batch Specifics).
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:
#PBS -N disable_multithreading #PBS -l walltime=00:10:00 #PBS -l nodes=1:ppn=20 #PBS -j oe module load matlab matlab -singleCompThread -nodisplay -nodesktop < hello.m # end of example file
Parallel computing across multiple nodes
Parallel computing across multiple nodes is only available on Oakley.
Usage on Owens
Set-up
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 -nodisplayIf 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:qsub -I -X -l nodes=1:ppn=28 -l walltime=00:20:00
which requests one whole node with 28 cores ( -l nodes=1:ppn=28 ), for a walltime of 20 minutes ( -l walltime=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. The –X flag enables X11 forwarding on the compute node, so you can use the MATLAB GUI if you choose.You may adjust the numbers per your need.
Parallel Processing in MATLAB
MATLAB supports both implicit multiprocessing (multithreading) and explicit multiprocessing across multiple nodes.
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. For the normal nodes, this mean matlab will start 28 threads to run on the 28 cores of the node. This is due to the whole node scheduling policy of Owens (see Batch Specifics).
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:
#PBS -N disable_multithreading #PBS -l walltime=00:10:00 #PBS -l nodes=1:ppn=28 #PBS -j oe module load matlab matlab -singleCompThread -nodisplay -nodesktop < hello.m # end of example file
Parallel computing across multiple nodes
Parallel computing across multiple nodes is only available on Oakley.
MATLAB with a GPU
A GPU can be utilized for MATLAB. You can acquire a GPU by nodes=1:ppn=20;gpus=1 for Ruby or Owens (ppn=28 for Owens). 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):
Polyspace Bug Finder PolySpace_Bug_Finder_Engine Aerospace_Blockset Aerospace_Toolbox Antenna_Toolbox Audio_System_Toolbox Automated_Driving_Toolbox Bioinformatics_Toolbox Communications System Toolbox Computer Vision System Toolbox Control System Toolbox Curve_Fitting_Toolbox DSP System Toolbox Data Acquisition Toolbox Database_Toolbox Datafeed_Toolbox Econometrics_Toolbox RTW_Embedded_Coder Filter_Design_HDL_Coder Financial Instruments Toolbox Financial_Toolbox Fixed_Point_Toolbox Fuzzy Logic Toolbox Global Optimization Toolbox Simulink_HDL_Coder EDA_Simulator_Link Image_Acquisition_Toolbox Image Processing Toolbox Instrument Control Toolbox LTE System Toolbox MATLAB_Coder MATLAB_Builder_for_Java Compiler MATLAB Report Generator Mapping Toolbox Model Predictive Control Toolbox Model-Based Calibration Toolbox Neural_Network_Toolbox OPC_Toolbox Optimization_Toolbox Distrib_Computing_Toolbox Partial Differential Equation Toolbox Phased_Array_System_Toolbox Polyspace Code Prover Powertrain_Blockset RF_Blockset RF_Toolbox Risk_Management_Toolbox Robotics_System_Toolbox Robust Control Toolbox Signal Processing Toolbox SimBiology SimEvents Simscape Driveline Simscape Electronics Simscape Fluids Simscape Multibody Simscape Power Systems Simscape Virtual_Reality_Toolbox Simulink_Code_Inspector Real-Time_Workshop Simulink_Control_Design Simulink Design Optimization Simulink_Design_Verifier Simulink Desktop Real-Time Simulink_PLC_Coder XPC_Target Simulink Report Generator Simulink_Test Simulink Verification and Validation Excel_Link Stateflow Statistics and Machine Learning Toolbox Symbolic Math Toolbox System Identification Toolbox Trading_Toolbox Vehicle_Network_Toolbox Vision_HDL_Toolbox WLAN_System_Toolbox Wavelet_Toolbox
Further Reading
Official PDF documentation can be obtained from the MathWorks Website.