Documentation

## Processor-In-The-Loop Execution with the GPU Coder App

Use the processor-in-the-loop (PIL) execution to check the numerical behavior of the CUDA® code that you generate from MATLAB® functions. A PIL simulation, which requires target connectivity, compiles generated source code, and then downloads and runs object code on NVIDIA® GPU platforms. The results of the PIL simulation are transferred to MATLAB to verify the numerical equivalence of the simulation and the code generation results.

The PIL verification process is a crucial part of the design cycle to check that the behavior of the generated code matches the design. PIL verification requires an Embedded Coder® license.

### Note

When using PIL execution, make sure that the Benchmarking option in GPU Coder™ settings is false. Executing PIL with benchmarking results in compilation errors.

### Prerequisites

#### Target Board Requirements

• NVIDIA DRIVE or Jetson embedded platform.

• Ethernet crossover cable to connect the target board and host PC (if the target board cannot be connected to a local network).

• NVIDIA CUDA toolkit installed on the board.

• Environment variables on the target for the compilers and libraries. For information on the supported versions of the compilers and libraries and their setup, see Install and Setup Prerequisites for NVIDIA Boards.

#### Development Host Requirements

• GPU Coder for code generation. For an overview and tutorials, see the Getting Started with GPU Coder (GPU Coder) page.

• Embedded Coder.

• NVIDIA CUDA toolkit on the host.

• Environment variables on the host for the compilers and libraries. For information on the supported versions of the compilers and libraries, see Third-party Products (GPU Coder). For setting up the environment variables, see Environment Variables (GPU Coder).

### Example: The Mandelbrot Set

#### Description

You do not have to be familiar with the algorithm in the example to complete the tutorial.

The Mandelbrot set is the region in the complex plane consisting of the values z0 for which the trajectories defined by

${z}_{k+1}={z}_{k}^{2}+{z}_{0},\text{ }k=0,\text{\hspace{0.17em}}1,\text{ }\text{\hspace{0.17em}}\dots$

remain bounded at k→∞. The overall geometry of the Mandelbrot set is shown in the figure. This view does not have the resolution to show the richly detailed structure of the fringe just outside the boundary of the set. At increasing magnifications, the Mandelbrot set exhibits an elaborate boundary that reveals progressively finer recursive detail.

#### Algorithm

Create a MATLAB script called mandelbrot_count.m with the following lines of code. This code is a baseline vectorized MATLAB implementation of the Mandelbrot set.

function count = mandelbrot_count(maxIterations, xGrid, yGrid) %#codegen
% mandelbrot computation

z0 = xGrid + 1i*yGrid;
count = ones(size(z0));

% Add Kernelfun pragma to trigger kernel creation
coder.gpu.kernelfun;

z = z0;
for n = 0:maxIterations
z = z.*z + z0;
inside = abs(z)<=2;
count = count + inside;
end
count = log(count);

For this tutorial, pick a set of limits that specify a highly zoomed part of the Mandelbrot set in the valley between the main cardioid and the p/q bulb to its left. A 1000x1000 grid of real parts (x) and imaginary parts (y) is created between these two limits. The Mandelbrot algorithm is then iterated at each grid location. An iteration number of 500 is enough to render the image in full resolution. Create a MATLAB script called mandelbrot_test.m with the following lines of code. It also calls the mandelbrot_count function and plots the resulting Mandelbrot set.

maxIterations = 500;
gridSize = 1000;
xlim = [-0.748766713922161, -0.748766707771757];
ylim = [ 0.123640844894862,  0.123640851045266];

x = linspace( xlim(1), xlim(2), gridSize );
y = linspace( ylim(1), ylim(2), gridSize );
[xGrid,yGrid] = meshgrid( x, y );

count = mandelbrot_count(maxIterations, xGrid, yGrid);

figure(1)
imagesc( x, y, count );
colormap([jet();flipud( jet() );0 0 0]);
axis off
title('Mandelbrot set');

### GPU Coder App

To open the GPU Coder app, on the MATLAB toolstrip Apps tab, under Code Generation, click the GPU Coder app icon. You can also open the app by typing gpucoder in the MATLAB Command Window.

1. The app opens the Select source files page. Select mandelbrot_count.m as the entry-point function. Click Next.

2. In the Define Input Types window, enter mandelbrot_count(500,zeros(1000),zeros(1000)) and click , then click Next.

3. You can initiate the Check for Run-Time Issues process or click Next to go to the Generate Code step.

4. Set the Build type to Static Library and the Hardware Board to NVIDIA Jetson.

5. Under the Hardware panel, enter the device address, user name, password, and build folder for the board.

6. Close the Settings window and click . The software generates CUDA code for the mandelbrot_count entry point function.

7. Click Verify Code.

8. In the command field, specify the test file that calls the original MATLAB functions. For example, mandelbrot_test.

9. To start the PIL execution, click Run Generated Code.

The GPU Coder app:

• Generates a standalone library, for example, codegen\lib\mandelbrot_count.

• Generates PIL interface code, for example, codegen\lib\mandelbrot_count\pil.

• Runs the test file, replacing calls to the MATLAB function with calls to the generated code in the library.

• Displays messages from the PIL execution in the Test Output tab.

### Note

On a Microsoft®Windows® system, the Windows Firewall can potentially block a PIL execution. Change the Windows Firewall settings to allow access.

10. Verify that the results from the PIL execution match the results from the original MATLAB functions.

11. To terminate the PIL execution process, click Stop PIL Verification. Alternatively, on the Test Output tab, click the link that follows To terminate execution.

## More About

#### Generating CUDA Code from MATLAB: Accelerating Embedded Vision and Deep Learning Algorithms on GPUs

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