root/OptimizingMATLABCode/Truss/clusterBatch.m @ 10
| 10 | anderm8 | %% Batch processing demo utilizing the ODE System example
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% Copyright 2015 The MathWorks, Inc.
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%% Close any open pool of workers (if it is on the cluster, an open pool might cause your job the be queued instead of running)
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delete(gcp('nocreate'));
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%% Submit job (1 worker)
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fprintf('Submitting batch job on 1 worker... ');
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% batch(function, number of outputs, inputs, profile, pool size)
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job1 = batch('paramSweepSerial', 4, {10, 10});
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fprintf('Submitted Job 1!\n');
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%% Submit job (30 workers)
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fprintf('Submitting batch job on 30 workers... ');
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job2 = batch('paramSweepParallel', 4, {50, 50}, 'Pool', 30);
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fprintf('Submitted Job 2!\n');
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%% Plot data when job is finished
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% See the function definition for paramSweepParallel.m for the definition
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% of the four output variables
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% Make sure job2 is finished and fetch results
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wait(job2);
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jobOutput2=fetchOutputs(job2);
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% Visualize results
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visualizeParamSweep(jobOutput2);
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%% Compare Timings
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% Make sure job1 is finished and fetch results
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wait(job1);
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jobOutput1=fetchOutputs(job1);
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fprintf('Computation time on 1 worker : %0.2f sec\n', jobOutput1{4});
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fprintf('Computation time on 30 workers: %0.2f sec\n', jobOutput2{4});
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%% Clean Up
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% These jobs are using physical resources. It's a best practice to delete
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% them when done
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delete(job1);
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delete(job2);
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