Use Dynamically Allocated C++ Arrays in Generated Function Interfaces - MATLAB & Simulink (original) (raw)

In most cases, when you generate code for a MATLAB® function that accepts or returns an array, there is an array at the interface of the generated CUDA® function. For an array size that is unknown at compile time, or whose bound exceeds a predefined threshold, the memory for the generated array is dynamically allocated.

By default, the dynamically allocated array is implemented by using the C styleemxArray data structure in the generated code. Alternatively, dynamically allocated array can be implemented as a class template calledcoder::gpu_array in the generated code.coder::gpu_array offers several advantages overemxArray style data structures:

• The generated code is exception safe.
• Generated code is easier to read.
• Better C++ integration because of ease of initializing the input data and working with the output data.
• Because coder::gpu_array is defined in a header file that ships with MATLAB, you can write the interface code before the generating code.

To use dynamically allocated arrays in your custom CUDA code that you integrate with the generated CUDA C++ functions, learn to use the coder::gpu_array template.

Change Interface Generation

By default, the generated CUDA code uses the C style emxArray data structure to implement dynamically allocated arrays. Instead, you can choose to generate CUDA code that uses the coder::gpu_array template to implement dynamically allocated arrays. To generate the coder::gpu_array template, do one of the following:

• In a code configuration object (coder.MexCodeConfig, coder.CodeConfig, or coder.EmbeddedCodeConfig), set theDynamicMemoryAllocationInterface parameter to'C++'.
• In the GPU Coder™ app settings, on the Memory tab, setDynamic memory allocation interface to Use C++ coder::array.

Using the coder::gpu_array Class Template

When you generate CUDA code for your MATLAB functions, the code generator produces header filescoder_gpu_array.h and coder::array.h in the build folder. The coder_gpu_array.h header file contains the definition of the class template gpu_array in the namespace coder and the definitions for the function templates arrayCopyCpuToGpu andarrayCopyGpuToCpu. The coder::gpu_array template implements the dynamically allocated arrays in the generated code. The declaration for this template is:

template <typename T, int32_T N> class gpu_array

The array contains elements of type T and has N dimensions. For example, to declare a two-dimensional dynamic arraymyArray that contains elements of type int32_T in your custom CUDA code, use:

coder::gpu_array<int32_T, 2> myArray

The function templates arrayCopyCpuToGpu andarrayCopyGpuToCpu implement data transfers between the CPU and GPU memory. On the CPU, the dynamically allocated arrays are implemented by using thecoder::array template. For more information on the APIs you use to create and interact with dynamic arrays in your custom code, see Use Dynamically Allocated C++ Arrays in Generated Function Interfaces.

To use dynamically allocated arrays in your custom CUDA code that you want to integrate with the generated code (for example, a custom main function), include the coder_gpu_array.h andcoder_array.h header files in your custom .cu files.

Generate C++ Code That Accepts and Returns a Variable-Size Numeric Array

This examples shows how to customize the generated example main function to use thecoder::gpu_array and coder::array class templates in your project.

Your goal is to generate a CUDA executable for xTest1 that can accept and return an array of int32_T elements. You want the first dimension of the array to be singleton and the second dimension to be unbounded.

1. Define a MATLAB function xTest1 that accepts an arrayX, adds the scalar A to each of its elements, and returns the resulting array Y.
   function Y = xTest1(X, A)
   Y = X;
   for i = 1:numel(X)
   Y(i) = X(i) + A;
   end
2. Generate initial source code for xTest1 and movexTest1.h from the code generation folder to your current folder. Use the following commands:
   cfg = coder.gpuConfig('lib');
   cfg.DynamicMemoryAllocationInterface = 'C++';
   cfg.GenerateReport = true;
   inputs = {coder.typeof(int32(0), [1 inf]), int32(0)};
   codegen -config cfg -args inputs xTest1.m
   The function prototype for xTest1 in the generated code is shown here:
   extern void xTest1(const coder::array<int, 2U> &X, int A,
   coder::array<int, 2U> &Y);

Interface the generated code by providing input and output arrays that are compatible with the function prototype shown above. 3. Define a CUDA main function in the file xTest1_main.cu in your current working folder.
This main function includes the header files coder_gpu_array.h and coder_array.h that contain thecoder::gpu_array and coder::array class template definitions respectively. The main function performs these actions:

• Declare myArray and myResult as two-dimensional coder::array dynamic arrays ofint32_T elements.
• Dynamically set the sizes of the two dimensions ofmyArray to 1 and 100 by using the set_size method.
• Access the size vector of myResult by usingmyResult.size.

#include
#include<coder_array.h>
#include<xTest1.h>
int main(int argc, char *argv[])
{
static_cast(argc);
static_cast(argv);

// Instantiate the input variable by using coder::array template  
coder::array<int32_T, 2> myArray;  
  
// Allocate initial memory for the array  
myArray.set_size(1, 100);  
// Access array with standard C++ indexing  
for (int i = 0; i < myArray.size(1); i++) {  
    myArray[i] = i;  
}  
  
// Instantiate the result variable by using coder::array template  
coder::array<int32_T, 2> myResult;  
// Pass the input and result arrays to the generated function  
xTest1(myArray, 1000, myResult);  
for (int i = 0; i < myResult.size(1); i++) {  
    if (i > 0) std::cout << " ";  
    std::cout << myResult[i];  
    if (((i+1) % 10) == 0) std::cout << std::endl;  
}  
std::cout << std::endl;  
return 0;  

} 4. Generate code by running this script:
cfg = coder.gpuConfig('exe');
cfg.DynamicMemoryAllocationInterface = 'C++';
cfg.GenerateReport = true;
cfg.CustomSource = 'xTest1_main.cu';
cfg.CustomInclude = '.';
codegen -config cfg -args inputs xTest1_main.cu xTest1.m 5. The code generator produces an executable file xTest1 in your current working folder. Run the executable using the following commands:
if ispc
!xtest1.exe
else
!./xTest1
end
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Limitations

• For generating CUDA code that uses coder::gpu_array, the GPU memory allocation mode must be set to discrete.
  To change the memory allocation mode in the GPU Coder app settings, in the GPU Code section, use theMalloc mode parameter. When using the command-line interface, use the MallocMode build configuration property and set it to either'discrete' or 'unified'.
• GPU Coder does not support coder::gpu_array in Simulink®.

See Also

coder.typeof | coder.varsize

Topics

• Use Dynamically Allocated C Arrays in the Generated Function Interfaces
• Representation of Arrays in Generated Code