You can use GPU Coder™ to speed up the execution of your Simulink® model by using NVIDIA® GPUs. If you have algorithms written in MATLAB® code, you can include the MATLAB code in a Simulink model or subsystem by using the MATLAB Function (Simulink) block. When you simulate a model that contains a MATLAB Function block, the software generates CUDA® MATLAB executable (MEX) code from the block and dynamically links the generated code to Simulink. For more information on code generation workflows supported by GPU Coder, see GPU Code Generation Workflow.
The basic steps for simulation acceleration by using GPU are:
Create or open a model
Configure the model for GPU acceleration by selecting the solver, language, and other GPU-specific configuration parameters
Run the GPU accelerated model
The Sobel edge detection algorithm is a popular yet simple edge detection algorithm that performs a 2-D spatial gradient operation on a grayscale image. This operation emphasizes the high spatial frequency regions which corresponds to the edges of the input image.
The Sobel edge algorithm computes the horizontal gradient (
H) and the
vertical gradient (
V) of the input image by using two orthogonal filter
k'). After the filtering operation, the
algorithm computes the gradient magnitude and applies a threshold to find the regions of the
images that are considered to be
k = single([1 2 1; 0 0 0; -1 -2 -1]); H = conv2(single(grayImage),k, 'same'); V = conv2(single(grayImage),k','same'); E = sqrt(H.*H + V.*V); edgeImage = uint8((E > threshold) * 255);
Create a new Simulink model and insert two MATLAB Function blocks from the User-Defined Functions library.
Open the Block Parameters dialog for the From Multimedia
File block and set the File name parameter to
Set the Image signal parameter to
Add two Video Viewer (Computer Vision Toolbox) blocks from the Computer Vision Toolbox library to the model.
Double-click on one of the MATLAB Function blocks. A default function signature appears in the MATLAB Function Block Editor.
Define a function called
sobel, which implements the Sobel edge
detection algorithm. The function header declares
threshold as an argument to the
edgeImage as the return value. Save Editor document to
function edgeImage = sobel(grayImage,threshold) %#codegen % Define Kernel for Sobel edge detection k = single([1 2 1; 0 0 0; -1 -2 -1]); % Detect Edge H = conv2(single(grayImage),k, 'same'); V = conv2(single(grayImage),k','same'); E = sqrt(H.*H + V.*V); edgeImage = uint8((E > threshold) * 255); end
Right-click on the MATLAB Function block and select
Block Parameters (Subsystem).
On the Code Generation tab, select
function for Function packaging.
Repeat steps 3 and 4 for the other MATLAB Function block. This block implements the RGB to grayscale conversion prior to the Sobel edge detection operation.
function gray = RGB2gray(RGB) %#codegen % Convert color image to grey image gray = (0.2989 * double(RGB(:,:,1)) + ... 0.5870 * double(RGB(:,:,2)) + ... 0.1140 * double(RGB(:,:,3))); end
Connect these blocks as shown in the diagram. Save the model as
To test for errors, simulate the model in the Simulink Editor using the Run button on the toolstrip. To see all video frames during simulation, disable the Simulation > Drop Frames to improve Performance option of the Video Viewer block.
Model configuration parameters determine the acceleration method used during simulation.
Open the Configuration Parameters dialog box, Solver pane. To compile your model for acceleration and generate CUDA code, you must configure the model to use a fixed-step solver. The following table shows the solver configuration for this example.
|Parameter||Setting||Effect on Generated Code|
|Type||Maintains a constant (fixed) step size, which is required for code generation|
|Solver||Applies a fixed-step integration technique for computing the state derivative of the model|
|Fixed-step size||Simulink chooses the step size|
Select the Simulation Target pane. Select GPU acceleration.
The Language parameter is automatically set to
GPU Coder specific options are now visible in the Simulation Target > GPU Acceleration pane. For the purposes of this example, you can use the default values of these parameters.
Click OK to save and close the Configuration Parameters dialog box.
Alternatively, you can use
set_param (Simulink) to configure the model parameter programmatically from the
MATLAB command Window. For
To build the GPU accelerated model and simulate it, you can start the model by selecting Run on the Simulation tab or by running the command:
at the MATLAB prompt.
The software first checks to see if CUDA code was previously compiled for the model. If code was created previously,
the software runs the model. If code was not previously built, the software first generates
and compiles the CUDA code, and then runs the model. The code generation tool places the generated
code in a subfolder of the working folder called
Use of MATLAB Function blocks in Stateflow® charts is not supported.
When GPU acceleration is enabled, the code generator does not
support Import custom code for importing custom authored
CUDA source files (*.cu). Instead, use
coder.ceval inside the MATLAB Function block.
MATLAB Function block does not support all the data types from the MATLAB language. For supported data types, refer to the block documentation.
rtwbuild(Simulink Coder) |