MATLAB
Getting Started with Parallel Computing using MATLAB on the Komondor HPC Cluster
This document provides the steps to configure MATLAB to submit jobs to a cluster, retrieve results, and debug errors.
CONFIGURATION – MATLAB client on the cluster
After logging into the cluster, start MATLAB. On the Home tab, click Parallel > Discover Clusters… to discover the profile.
Jobs will now default to the cluster rather than submit to the local machine.
INSTALLATION and CONFIGURATION – MATLAB client on the desktop
The Komondor MATLAB support package can be downloaded from this link.
Download the ZIP file and start MATLAB. The ZIP file should be unzipped in the location returned by calling
>> userpath
Configure MATLAB to run parallel jobs on the cluster by calling configCluster. configCluster only needs to be called once per version of MATLAB.
>> configCluster
Submission to the cluster requires SSH credentials. You will be prompted for username and password or identity file (private key). The username and location of the private key will be stored in MATLAB for future sessions.
Jobs will now default to the cluster rather than submit to the local machine.
NOTE: To submit to the local machine instead of the cluster, run the following:
>> % Get a handle to the local resources
>> c = parcluster("local");
CONFIGURING JOBS
Prior to submitting the job, various parameters can be assigned, such as queue, e-mail, walltime, etc. The following is a partial list of parameters. See AdditionalProperties for the complete list. [Only AccountName is required.]
>> % Get a handle to the cluster
>> c = parcluster;
[REQUIRED]
>> % Specify the account
>> c.AdditionalProperties.AccountName = "account-name";
[OPTIONAL]
>> % Specify a constraint
>> c.AdditionalProperties.Constraint = "feature-name";
>> % Request email notification of job status
>> c.AdditionalProperties.EmailAddress = "user-id@university.edu";
>> % Specify number of GPUs (default: 0)
>> c.AdditionalProperties.GPUsPerNode = 1;
>> c.AdditionalProperties.GPUCard = "gpu-card";
>> % Specify memory to use, per core (default: 4gb)
>> c.AdditionalProperties.MemPerCPU = "6gb";
>> % Specify the partition
>> c.AdditionalProperties.Partition = "partition-name";
>> % Specify cores per node
>> c.AdditionalProperties.ProcsPerNode = 4;
>> % Set node exclusivity (default: false)
>> c.AdditionalProperties.RequireExclusiveNode = true;
>> % Use reservation
>> c.AdditionalProperties.Reservation = "reservation-name";
>> % Specify the wall time (e.g., 1 day, 5 hours, 30 minutes)
>> c.AdditionalProperties.WallTime = "1-05:30";
Save changes after modifying AdditionalProperties for the above changes to persist between MATLAB sessions.
>> c.saveProfile
To see the values of the current configuration options, display AdditionalProperties.
>> % To view current properties
>> c.AdditionalProperties
Unset a value when no longer needed.
>> % Turn off email notifications
>> c.AdditionalProperties.EmailAddress = '';
>> c.saveProfile
INTERACTIVE JOBS - MATLAB client on the cluster
To run an interactive pool job on the cluster, continue to use parpool as before.
>> % Get a handle to the cluster
>> c = parcluster;
>> % Open a pool of 64 workers on the cluster
>> pool = c.parpool(64);
Rather than running local on the local machine, the pool can now run across multiple nodes on the cluster.
>> % Run a parfor over 1000 iterations
>> parfor idx = 1:1000
a(idx) = rand;
end
Delete the pool when it’s no longer needed.
>> % Delete the pool
>> pool.delete
INDEPENDENT BATCH JOB
Use the batch command to submit asynchronous jobs to the cluster. The batch command will return a job object which is used to access the output of the submitted job. See the MATLAB documentation for more help on batch.
>> % Get a handle to the cluster
>> c = parcluster;
>> % Submit job to query where MATLAB is running on the cluster
>> job = c.batch(@pwd, 1, {}, "CurrentFolder",'.');
>> % Query job for state
>> job.State
>> % If state is finished, fetch the results
>> job.fetchOutputs{:}
>> % Delete the job after results are no longer needed
>> job.delete
To retrieve a list of running or completed jobs, call parcluster to return the cluster object. The cluster object stores an array of jobs that are queued to run, are running, have run, or have failed. Retrieve and view the list of jobs as shown below.
>> c = parcluster;
>> jobs = c.Jobs
>>
>> % Get a handle to the second job in the list
>> job2 = c.Jobs(2);
Once the job has been selected, fetch the results as previously done.
fetchOutputs is used to retrieve function output arguments; if calling batch with a script, use load instead. Data that has been written to files on the cluster needs be retrieved directly from the file system (e.g., via sftp).
>> % Fetch all results from the second job in the list
>> job2.fetchOutputs{:}
PARALLEL BATCH JOB
batch can also submit parallel workflows. Let’s use the following example for a parallel job, which is saved as parallel_example.m.
function [sim_t, A] = parallel_example(iter)
if nargin==0
iter = 8;
end
disp("Start sim")
t0 = tic;
parfor idx = 1:iter
A(idx) = idx;
pause(2)
idx
end
sim_t = toc(t0);
disp("Sim completed")
save RESULTS A
end
This time when using the batch command, also specify a MATLAB Pool argument.
>> % Get a handle to the cluster
>> c = parcluster;
>> % Submit a batch pool job using 4 workers for 16 simulations
>> job = c.batch(@parallel_example, 1, {16}, "Pool",4, "CurrentFolder",'.');
>> % View current job status
>> job.State
>> % Fetch the results after a finished state is retrieved
>> job.fetchOutputs{:}
ans =
8.8872
The job ran in 8.89 seconds using four workers. Note that these jobs will always request N+1 CPU cores, since one worker is required to manage the batch job and pool of workers. For example, a job that needs eight workers will request nine CPU cores.
Run the same simulation but increase the Pool size. This time, to retrieve the results later, keep track of the job ID.
NOTE: For some applications, there will be a diminishing return when allocating too many workers, as the overhead may exceed computation time.
>> % Get a handle to the cluster
>> c = parcluster;
>> % Submit a batch pool job using 8 workers for 16 simulations
>> job = c.batch(@parallel_example, 1, {16}, "Pool",8, "CurrentFolder",'.');
>> % Get the job ID
>> id = job.ID
id =
4
>> % Clear job from workspace (as though MATLAB exited)
>> clear job
With a handle to the cluster, the findJob method searches for the job with the specified job ID.
>> % Get a handle to the cluster
>> c = parcluster;
>> % Find the old job
>> job = c.findJob("ID", 4);
>> % Retrieve the state of the job
>> job.State
ans =
finished
>> % Fetch the results
>> job.fetchOutputs{:};
ans =
4.7270
The job now runs in 4.73 seconds using eight workers. Run code with different number 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).
HELPER FUNCTIONS
Function |
Description |
Applies only to Desktop |
|---|---|---|
clus terFeatures |
List of cluster features/constraints |
|
clus terGpuCards |
List of cluster GPU cards |
|
clusterPar titionNames |
List of cluster partitions |
|
disab leArchiving |
Modify file archiving to resolve file mirroring issue |
true |
fi xConnection |
Reestablish cluster connection (e.g., after reconnection of VPN) |
true |
qosLimits |
List of QoS limits for user |
|
willRun |
Explain why job is queued |
DEBUGGING
If a serial job produces an error, call the getDebugLog method to view the error log file. When submitting an independent job, specify the task.
>> c.getDebugLog(job.Tasks)
For Pool jobs, only specify the job object.
>> c.getDebugLog(job)
When troubleshooting a job, the cluster admin may request the scheduler ID of the job. This can be derived by calling getTaskSchedulerIDs (call schedID(job) before R2019b).
>> job.getTaskSchedulerIDs()
ans =
25539
TO LEARN MORE
To learn more about the MATLAB Parallel Computing Toolbox, check out these resources: