MATLAB: How to delete the completed and running jobs alloted to workers in Parallel Computing Toolbox 4.1 (R2009a)

canceldeletedestroyjobsParallel Computing Toolbox

I am trying to delete the completed jobs and want to stop some running jobs while using Parallel Computing Toolbox 4.1 (R2009a). Please let me know the commands for the same and point me to the documentation link.

Best Answer

Please see below for the commands and related documentation links.

1.You can use the DESTROY command to delete your completed jobs and the running jobs for all MATLAB versions after MATLAB R14sp3.

You can find more information about the DESTROY command at the link below:

 <http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/destroy.html>

2.The CANCEL command cancels your job or task from pending, running or queued state.

You can find more information about the CANCEL command at the link below:

 <http://www.mathworks.com/access/helpdesk/help/toolbox/distcomp/index.html?/access/helpdesk/help/toolbox/distcomp/cancel.html>

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MATLAB: Do I need to have a pre-existing “cluster” to use the Distrubuted Computing Toolbox and MATLAB Distributed Computing Engine

This enhancement has been incorporated in Release 2006a (R2006a). For previous product releases, read below for any possible workarounds:

The computers used for distributed computing do not require any pre-existing "clustering" software. The MATLAB Distributed Computing Engine (MDCE) enables computers on the same network to act as a cluster, in that tasks are distributed across several independent machines running the MDCE. An actual "cluster," running cluster management software, is not required to use the product.

MATLAB: How to use the Parallel Computing Toolbox 4.1 (R2009a) to find the singular value decomposition of a large matrix

To determine the singular value decomposition (svd) of a large matrix in the Parallel Computing Toolbox 4.1 (R2009a) without using pmode, create a MATLAB function file that creates a codistributed matrix and finds the svd of the matrix.

The concept is illustrated using an example given below:

function y = thisJob(n)
x = rand(n,codistributor());
y = svd(x);

Execute the following commands to run the above job on the worker machines:

main.m

An example of math with a codistributed matrix in non-pmode

% Find the available parallel computing resources
sched = findResource('scheduler','type','local');
% Construct a parallel job object using the resources
pjob = createParallelJob(sched);
% Set the dimension of the random matrix
a = 16;
% Set the file thisJob.m so that the workers can access it.
pjob.FileDependencies={'thisJob.m'}
% Add the task to the job.
createTask(pjob,@thisJob, 1, {a});
% Run the job.
submit(pjob);
% Retrieve job results.
waitForState(pjob);
out = getAllOutputArguments(pjob);
% Display the random matrices.
%  Note:  the output values in each worker will all be the same, they
%  represent the singular values of the matrix.
out{1}
% Destroy the job.
destroy(pjob);

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