MATLAB: What results in the performance improvement when simulating SimBiology models in SimBiology 3.1 (R2009b)

SimBiology

In the documentation for SimBiology 3.1 (R2009b), the release notes state "increased performance when repeatedly simulating a model." I want to know whether the performance improvement is a result of compiling the model, which is then run repeatedly.

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SimBiology 3.1 (R2009b) improves performance by caching the compiled state of the model after it is first simulated. This cached state includes a MATLAB language representation of the model equations. SimBiology does not yet compile models to C-code.

Note that the sort of changes to a model that will not cause a recompile are changes to species initial amounts, parameter values, and compartment capacities. The only other change that will not cause a recompile is a change to the stop time of a simulation.

Related Solutions

MATLAB: Any simulation speed differences between models loaded with SimBiology() vs sbioloadproject

I don't expect you to see any noticeable performance impact when simulating a model from the command line, while the same model is open in the SimBiology app. When you modify a model from the command line, there is some overhead in communicating model changes to the model to the app. I can imagine some rare cases where that overhead could be noticeable.

So, until MathWorks gives you a better option (which is definitely on our radar), I suggest you do what you propose: Keep the model open in the app while working with it from the command line.

MATLAB: How does SimBiology 3.1 (R2009b) handle memory management when passing a simbio model object to a function that modifies it and then returns it

SimBiology 3.1 (R2009b) models are handle objects, which means that they are passed by reference. Therefore, any changes made to a model will be visible to everyone who has that handle. Unlike some other MATLAB handle objects, all SimBiology models automatically have a handle added to the SimBiology root object. This prevents the user from losing a model by clearing or overwriting a variable. However, this does highlight an important consideration for memory management: to remove a SimBiology model from memory, it must be explicitly deleted with a command like

delete(m1)

All SimBiology models in memory can also be deleted at once by using the function SBIORESET.

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Related Solutions

MATLAB: Any simulation speed differences between models loaded with SimBiology() vs sbioloadproject

MATLAB: How does SimBiology 3.1 (R2009b) handle memory management when passing a simbio model object to a function that modifies it and then returns it

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