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MATLAB combines a desktop environmenttuned for iterative analysis and design processes with a programminglanguage that expresses matrix and array mathematics directly.
Using MATLAB on Cirrus¶
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MATLAB R2018b is available on Cirrus.
This installation of MATLAB on Cirrus is covered by an AcademicLicense - for use in teaching, academic research, and meeting courserequirements at degree granting institutions only. Not forgovernment, commercial, or other organizational use.
If your use of MATLAB is not covered by this license then please donot use this installation. Please contact the Cirrus Helpdesk to arrange use of your ownMATLAB license on Cirrus.
This is MATLAB Version 9.5.0.1033004 (R2018b) Update 2 and provides thefollowing toolboxes
Running MATLAB jobs¶
On Cirrus, MATLAB is intended to be used on the compute nodes withinSlurm job scripts. Use on the login nodes should be restricted tosetting preferences, accessing help, and launching MDCS jobs. It isrecommended that MATLAB is used without a GUI on the compute nodes, asthe interactive response is slow.
Running parallel MATLAB jobs using the local cluster¶
The license for this installation of MATLAB provides only 32 workersvia MDCS but provides 36 workers via the local cluster profile (thereare 36 cores on a Cirrus compute node), so we only recommend the useof MDCS to test the configuration of distributed memory parallelcomputations for eventual use of your own MDCS license.
The local cluster should be used within a Slurm job script - yousubmit a job that runs MATLAB and uses the local cluster, which isthe compute node that the job is running on.
MATLAB will normally use up to the total number of cores on a node formulti-threaded operations (e.g. matrix inversions) and for parallelcomputations. It also make no restriction on its memory use. Thesefeatures are incompatible with the shared use of nodes on Cirrus. Forthe local cluster, a wrapper script is provided to limit the numberof cores and amount of memory used, in proportion to the number ofCPUs selected in the Slurm job script. Please use this wrapper insteadof using MATLAB directly.
Say you have a job that requires 3 workers, each running 2 threads.As such, you should employ 3x2=6 cores. An example job script forthis particular case would be
Note, for MATLAB versions R2019 and later, the matlab_wrapper_2019 script may be required(see 2019 section below).
This would run the testp.m script, without a display, and exit whentestp.m has finished. 6 CPUs are selected, which correspond to 6cores, and the following limits would be set initially
The testp.m program sets NumWorkers to 3 and NumThreads to 2
Note that PreferredNumWorkers, NumWorkers and NumThreads persistbetween MATLAB sessions but will be updated correctly if you use thewrapper each time.
NumWorkers and NumThreads can be changed (using parclusterand saveProfile) but NumWorkers * NumThreads should be less thanor equal to the number of cores (ncores above). If you wish aworker to run a threaded routine in serial, you must set NumThreadsto 1 (the default).
If you specify exclusive node access, then all the cores and memorywill be available. On the login nodes, a single core is used andmemory is not limited.
MATLAB 2019 versions¶
There has been a change of configuration options for MATLAB from version R2019 and onwardsthat means the -r flag has been replaced with the -batch flag. To accommodate that a newjob wrapper script is required to run applications. Zoom it 1 0 – on screen magnifier. For these versions ofMATLAB, if you need to use the -r or -batch flag replace this line in your Slurm script, i.e.:
with:
and this should allow scripts to run normally. Sqlpro studio 1 0 418 l.
Running parallel MATLAB jobs using MDCS¶
It is possible to use MATLAB on the login node to set up an MDCSSlurm cluster profile and then launch jobs using that profile.However, this does not give per-job control of the number of cores andwalltime; these are set once in the profile.
This MDCS profile can be used in MATLAB on the login node - the MDCScomputations are done in Slurm jobs launched using the profile.
Configuration¶
Start MATLAB on the login node. Configure MATLAB to run parallel jobson your cluster by calling configCluster. For each cluster,configCluster only needs to be called once per version of MATLAB
Jobs will now default to the cluster rather than submit to the localmachine (the login node in this case).
Configuring jobs¶
Prior to submitting the job, you can specify various parameters topass to our jobs, such as walltime, e-mail, etc. Other thanProjectCode and WallTime, none of these are required to be set.
NOTE: Any parameters specified using this workflow will be persistentbetween MATLAB sessions
Save changes after modifying AdditionalProperties fields
To see the values of the current configuration options, call thespecific AdditionalProperties name
To clear a value, assign the property an empty value (‘', [], or false)
Interactive jobs¶
To run an interactive pool job on the cluster, use parpool asbefore. configCluster sets NumWorkers to 32 in the cluster tomatch the number of MDCS workers available in our TAH licence. If youhave your own MDCS licence, you can change this by settingc.NumWorkers and saving the profile.
Rather than running locally on one compute node machine, this poolcan run across multiple nodes on the cluster
Once you have finished using the pool, delete it
Serial jobs¶
Rather than running interactively, use the batch command to submitasynchronous jobs to the cluster. This is generally more useful onCirrus, which usually has long queues. The batch command willreturn a job object which is used to access the output of thesubmitted job. See the MATLAB documentation for more help onbatch
To retrieve a list of currently running or completed jobs, callparcluster to retrieve the cluster object. The cluster objectstores an array of jobs that were run, are running, or are queued torun. This allows you to fetch the results of completed jobs. Retrieveand view the list of jobs as shown below
Once you have identified the job you want, you can retrieve theresults as you have done previously.
fetchOutputs is used to retrieve function output arguments; if usingbatch with a script, use load instead. Data that has been written tofiles on the cluster needs be retrieved directly from the file system.
To view results of a previously completed job
NOTE: You can view a list of your jobs, as well as their IDs, usingthe above c.Jobs command
NOTE: When submitting independent jobs, with multiple tasks, you willhave to specify the task number.
Parallel jobs¶
Users can also submit parallel workflows with batch. You can use thefollowing example (parallel_example.m) for a parallel job
Use the batch command again, but since you are running a paralleljob, you also specify a MATLAB Pool
The job ran in 8.89 seconds using 4 workers. Note that these jobs willalways request N+1 CPU cores, since one worker is required to manage thebatch job and pool of workers. For example, a job that needs eightworkers will consume nine CPU cores. With a MDCS licence for 32 workers,you will be able to have a pool of 31 workers.
Run the same simulation but increase the Pool size. This time, toretrieve the results later, keep track of the job ID.
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NOTE: For some applications, there will be a diminishing return whenallocating too many workers, as the overhead may exceed computationtime.
Once you have a handle to the cluster, call the findJob method tosearch for the job with the specified job ID
The job now runs 4.73 seconds using 8 workers. Run code with differentnumber of workers to determine the ideal number to use.
Alternatively, to retrieve job results via a graphical user interface,use the Job Monitor (Parallel > Monitor Jobs).
Debugging¶
If a serial job produces an error, you can call the getDebugLog method toview the error log file
When submitting independent jobs, with multiple tasks, you will have tospecify the task number. For Pool jobs, do not dereference into the jobobject
The scheduler ID can be derived by calling schedID
To learn more¶
fetchOutputs is used to retrieve function output arguments; if usingbatch with a script, use load instead. Data that has been written tofiles on the cluster needs be retrieved directly from the file system.
To view results of a previously completed job
NOTE: You can view a list of your jobs, as well as their IDs, usingthe above c.Jobs command
NOTE: When submitting independent jobs, with multiple tasks, you willhave to specify the task number.
Parallel jobs¶
Users can also submit parallel workflows with batch. You can use thefollowing example (parallel_example.m) for a parallel job
Use the batch command again, but since you are running a paralleljob, you also specify a MATLAB Pool
The job ran in 8.89 seconds using 4 workers. Note that these jobs willalways request N+1 CPU cores, since one worker is required to manage thebatch job and pool of workers. For example, a job that needs eightworkers will consume nine CPU cores. With a MDCS licence for 32 workers,you will be able to have a pool of 31 workers.
Run the same simulation but increase the Pool size. This time, toretrieve the results later, keep track of the job ID.
Onyx 3 4 2 – Maintenance And Optimization Toolbox
NOTE: For some applications, there will be a diminishing return whenallocating too many workers, as the overhead may exceed computationtime.
Once you have a handle to the cluster, call the findJob method tosearch for the job with the specified job ID
The job now runs 4.73 seconds using 8 workers. Run code with differentnumber of workers to determine the ideal number to use.
Alternatively, to retrieve job results via a graphical user interface,use the Job Monitor (Parallel > Monitor Jobs).
Debugging¶
If a serial job produces an error, you can call the getDebugLog method toview the error log file
When submitting independent jobs, with multiple tasks, you will have tospecify the task number. For Pool jobs, do not dereference into the jobobject
The scheduler ID can be derived by calling schedID
To learn more¶
To learn more about the MATLAB Parallel Computing Toolbox, check outthese resources:
GPUs¶
Calculations using GPUs can be done using the GPU nodes. This can be done using MATLAB within a Slurm jobscript, similar to using the local cluster, or can bedone using the MDCS profile. The GPUs are shared unlessyou request exclusive access to the node (4 GPUs), so you may findthat you share a GPU with another user.
