Setup

Start by ensuring the Simulation Data Inspector is empty and Parallel Computing Toolbox support is configured to import runs created on local workers automatically. Then, create a vector of filter parameter values to use in each simulation.

% Make sure the Simulation Data Inspector is empty, and PCT support is
% enabled. 
Simulink.sdi.clear
Simulink.sdi.enablePCTSupport('local')

% Define Ts values
Ts_vals = [0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]; 

Initialize Parallel Workers

Use gcp to create a pool of local workers to run parallel simulations if you don't already have one. In an spmd code block, load the slexAircraftExample model and select signals to log. To avoid data concurrency issues using sim in parfor, create a temporary directory for each worker to use during simulations.

p = gcp;

Starting parallel pool (parpool) using the 'local' profile ...
connected to 4 workers.

spmd
    
    % Load system and select signals to log
    load_system('slexAircraftExample')
    Simulink.sdi.markSignalForStreaming('slexAircraftExample/Pilot', 1, 'on')
    Simulink.sdi.markSignalForStreaming('slexAircraftExample/Aircraft Dynamics Model', 4, 'on')
    
    % Create temporary directory on each worker
    workDir = pwd;
    addpath(workDir)
    tempDir = tempname;
    mkdir(tempDir)
    cd(tempDir)
    
end

Run Parallel Simulations

Use parfor to run the seven simulations in parallel. Select the value for Ts for each simulation, and modify the value of Ts in the model workspace. Then, run the simulation and build an array of Simulink.sdi.WorkerRun objects to access the data with the Simulation Data Inspector. After the parfor loop, use another spmd segment to remove the temporary directories from the workers.

parfor index = 1:7
    
    % Select value for Ts
    Ts_val = Ts_vals(index);
    
    % Change the filter time constant and simulate
    modelWorkspace = get_param('slexAircraftExample','modelworkspace');
    modelWorkspace.assignin('Ts',Ts_val)
    sim('slexAircraftExample')
    
    % Create a worker run for each simulation
    workerRun(index) = Simulink.sdi.WorkerRun.getLatest
    
end

spmd
        
    % Remove temporary directories
    cd(workDir)
    rmdir(tempDir, 's')
    rmpath(workDir)
    
end

Get Dataset Objects from Parallel Simulation Output

The getDataset method puts the data from a WorkerRun into a Dataset object so you can easily post-process.

ds(7) = Simulink.SimulationData.Dataset;

for a = 1:7
    ds(a) = workerRun(a).getDataset;
end
ds(1)

ans = 
Simulink.SimulationData.Dataset '' with 2 elements

                         Name        BlockPath                                
                         __________  ________________________________________ 
    1  [1x1 Signal]      alpha, rad  ...rcraftExample/Aircraft Dynamics Model
    2  [1x1 Signal]      Stick       slexAircraftExample/Pilot               

  - Use braces { } to access, modify, or add elements using index.

Get DatasetRef Objects from Parallel Simulation Output

For big data workflows, use the getDatasetRef method to reference the data associated with the WorkerRun.

for b = 1:7
    datasetRef(b) = workerRun(b).getDatasetRef;
end

datasetRef(1)

ans = 
  DatasetRef with properties:

           Name: 'Run 3: slexAircraftExample'
            Run: [1×1 Simulink.sdi.Run]
    numElements: 2

Process Parallel Simulation Data in the Simulation Data Inspector

You can also create local Run objects to analyze and visualize your data using the Simulation Data Inspector API. This example adds a tag indicating the filter time constant value for each run.

for c = 1:7
    
    Runs(c) = workerRun(c).getLocalRun;
    Ts_val_str = num2str(Ts_vals(c));
    desc = strcat('Ts = ', Ts_val_str);
    Runs(c).Description = desc;
    Runs(c).Name = strcat('slexAircraftExample run Ts=', Ts_val_str);
    
end

Clean Up Worker Repositories

Clean up the files used by the workers to free up disk space for other simulations you want to run on your worker pool.

Simulink.sdi.cleanupWorkerResources