Run Simulink model parallel - matlab

Question

I would like to run a complex Simulink model in a parfor loop on many cores with different data.
However, I didn't have success yet so I created a simple model and tried to run it parallel with the same data.
The model looks like this, adding two signals:
With the code:
function Result = Add (X, Y)
Result = X + Y;
The script to run the model is the following:
if matlabpool('size') == 0
matlabpool('open',4);
end
parfor i = 1:4
data=ones(1,20);
X=timeseries(data);
Y=timeseries(data);
output = sim('model_test','StopTime','5');
Result = output.get('Res');
end
However, the following error occurs:
I don't understand why the variables are not existing. I know that paralell computing is always critical in terms of variable access, but I didn't have success with simulink parallel running yet. Can you please explain the error to me and how to solve it?
Thank you very much!
Answer to am304: Thank you, the answer helped me in the way that I now know how to change constants with set_param in the parfor loop and I understand why it doesn't work for timeseries.
However for timeseries I am still struggling.
I tried several versions, also this one:
if matlabpool('size') == 0
matlabpool('open',4);
end
data=ones(1,20);
X=timeseries(data);
Ybase=timeseries(data);
parfor i = 1:4
Y = evalin('base', 'Ybase');
output = sim('model_test','StopTime','5');
Result{i} = output.get('Res');
end
The variable Ybase exists in the workspace, but the following error occurs:
As you see, the variable Ybase exists in the base workspace. Do you know how to use evalin or assignin to access properly?
Thanks and regards!

Answer 1

I suspect it's because your data data only exists in the workspace of the main MATLAB, not in any of the instances fired up by matlabpool on the workers. Have a look at Workspace Access Issues in the documentation for more details on how to resolve this, with some examples illustrating the two approaches:
The MATLAB workers, however, do not have access to the workspace of
the MATLAB client session where the model and its associated workspace
variables have been loaded. Hence, if you load a model and define its
associated workspace variables outside of and before a parfor loop,
then neither is the model loaded, nor are the workspace variables
defined in the MATLAB worker sessions where the parfor iterations are
executed. This is typically the case when you define model parameters
or external inputs in the base workspace of the client session. These
scenarios constitute workspace access issues.
[...]
Resolving Workspace Access Issues
When a Simulink model is loaded into memory in a MATLAB client session, it is only visible and
accessible in that MATLAB session; it is not accessible in the memory
of the MATLAB worker sessions. Similarly, the workspace variables
associated with a model that are defined in a MATLAB client session
(such as parameters and external inputs) are not automatically
available in the worker sessions. You must therefore ensure that the
model is loaded and that the workspace variables referenced in the
model are defined in the MATLAB worker session by using the following
two methods.
In the parfor loop, use the sim command to load the model and to set parameters that change with each iteration. (Alternative:
load the model and then use the g(s)et_param command(s) to set the
parameters in the parfor loop)
In the parfor loop, use the MATLAB evalin and assignin commands to assign data values to variables. Alternatively, you can
simplify the management of workspace variables by defining them in
the model workspace. These variables will then be automatically
loaded when the model is loaded into the worker sessions. There
are, however, limitations to this method. For example, you cannot
have tunable parameters in a model workspace. For a detailed
discussion on the model workspace, see Model Workspaces.
EDIT
Here's what I would do in your particular example:
if matlabpool('size') == 0
matlabpool('open',4);
end
data=ones(1,20);
X=timeseries(data);
Y=timeseries(data);
parfor i = 1:4
assignin('base','Y',Y);
output = sim('model_test','StopTime','5');
Result{i} = output.get('Res');
end
Another option is to include X and Y in the model workspace so that the model is self-contained.