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| R2012a Documentation → Simulink Design Optimization | |
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You can use Simulink Design Optimization software with Parallel Computing Toolbox™ software to speed up parameter estimation of Simulink models. Using parallel computing may reduce the estimation time in the following cases:
The model contains a large number parameters to estimate, and the Nonlinear least squares or Gradient descent is selected as the estimation method.
The Pattern search method is selected as the estimation method.
The model is complex and takes a long time to simulate.
When you use parallel computing, Simulink Design Optimization software distributes independent simulations to run them in parallel on multiple MATLAB sessions, also known as workers. The time required to simulate the model dominates the total estimation time. Therefore, distributing the simulations significantly reduces the estimation time. For more information on the expected speedup, see How Parallel Computing Speeds Up Estimation.
The following sections describe how to configure your system, and use parallel computing:
You can enable parallel computing with the Nonlinear least squares, Gradient descent and Pattern search estimation methods in the Simulink Design Optimization software. The following sections describe how parallel computing speeds up the estimation:
When you select Gradient descent as the estimation method, the model is simulated during the following computations:
Objective value computation — One simulation per iteration
Objective gradient computations — Two simulations for every tuned parameter per iteration
Line search computations — Multiple simulations per iteration
The total time,
, taken per iteration to perform
these simulations is given by the following equation:
where
is
the time taken to simulate the model and is assumed to be equal for
all simulations,
is the number
of parameters to estimate, and
is
the number of line searches.
When you use parallel computing, Simulink Design Optimization software
distributes the simulations required for objective gradient computations.
The simulation time taken per iteration when the gradient computations
are performed in parallel,
,
is approximately given by the following equation:
where
is
the number of MATLAB workers.
Note The equation does not include the time overheads associated with configuring the system for parallel computing and loading Simulink software on the remote MATLAB workers. |
The expected reduction of the total estimation time is given by the following equation:
For example, for a model with Np=3, Nw=4,
and Nls=3, the expected
reduction of the total estimation time equals
.
The Pattern search method uses search and poll sets to create and compute a set of candidate solutions at each estimation iteration.
The total time,
, taken per iteration to perform
these simulations, is given by the following equation:
where
is
the time taken to simulate the model and is assumed to be equal for
all simulations,
is the number
of parameters to estimate,
is
a factor for the search set size, and
is a factor for the poll set
size.
When you use parallel computing, Simulink Design Optimization software
distributes the simulations required for the search and poll set computations,
which are evaluated in separate parfor loops. The simulation time taken per iteration
when the search and poll sets are computed in parallel,
, is given by the following equation:
where
is
the number of MATLAB workers.
Note The equation does not include the time overheads associated with configuring the system for parallel computing and loading Simulink software on the remote MATLAB workers. |
The expected speed up for the total estimation time is given by the following equation:
For example, for a model with Np=3, Nw=4, Nss=15,
and Nps=2, the expected
speedup equals
.
Using the Pattern search method with parallel computing may not speed up the estimation time. When you do not use parallel computing, the method stops searching for a candidate solution at each iteration as soon as it finds a solution better than the current solution. When you use parallel computing, the candidate solution search is more comprehensive. Although the number of iterations may be larger, the estimation without using parallel computing may be faster.
 | Accelerating Model Simulations During Estimation | How to Use Parallel Computing (GUI) |  |
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