Updated on Kwiecień 2, 2018

OpenCL tutorial – część 4

Na koniec przedstawię obiektowe API OpenCL dla języka C++.

Jedyne co musimy zrobić, to pobrać plik „cl.hpp” ze strony Khronosa i umieścić go w tym samym katalogu co plik „cl.h”, czyli „dependencies\OpenCL\include\CL”.

Poniższy przykład wykonuje takie same operacji jak program z poprzedniej części, czyli sumuje dwa wektory.

Kod pliku Main.cpp

#define __CL_ENABLE_EXCEPTIONS
#define __NO_STD_VECTOR

#include <iostream>
#include <fstream>
#include <string>

#include <CL/cl.hpp>

void randomizeArray(cl_int* data, size_t vectorSize)
{
    for (size_t i = 0; i < vectorSize; ++i) 
    {
        data[i] = rand() % 10;
    }
}

int main()
{
    try
    {
        // Allocate and initialize host arrays
        size_t vectorSize = 32;
        size_t localWorkSize = 8;

        cl_int* a = new cl_int[vectorSize];
        cl_int* b = new cl_int[vectorSize];
        cl_int* c = new cl_int[vectorSize];

        randomizeArray(a, vectorSize);
        randomizeArray(b, vectorSize);

        // Get platforms.
        cl::vector<cl::Platform> platforms;
        cl::Platform::get(&platforms);

        for (cl::vector<cl::Platform>::iterator i = platforms.begin(); i != platforms.end(); ++i)
        {
            cl::Platform platform = *i;
            std::string name;

            platform.getInfo(CL_PLATFORM_NAME, &name);

            std::cout << "Name:\t\t" << name << std::endl;

            platform.getInfo(CL_PLATFORM_VENDOR, &name);

            std::cout << "Vendor:\t\t" << name << std::endl;

            std::cout << std::endl;
        }

        // Create a context.
        cl_context_properties cps[3] =
        { 
            CL_CONTEXT_PLATFORM, 
            (cl_context_properties)(platforms[1])(), 
            0 
        };

        cl::Context context( CL_DEVICE_TYPE_GPU, cps);

        // Get devices.
        cl::vector<cl::Device> devices = context.getInfo<CL_CONTEXT_DEVICES>();

        for (cl::vector<cl::Device>::iterator i = devices.begin(); i != devices.end(); ++i)
        {
            cl::Device device = *i;
            std::string name;

            device.getInfo(CL_DEVICE_NAME, &name);

            std::cout << "Name:\t\t" << name << std::endl;

            device.getInfo(CL_DEVICE_VENDOR, &name);

            std::cout << "Vendor:\t\t" << name << std::endl;

            device.getInfo(CL_DEVICE_VERSION, &name);

            std::cout << "Version:\t" << name << std::endl;

            std::cout << std::endl;
        }

        // Create a command queue.
        cl::CommandQueue queue = cl::CommandQueue(context, devices[0]);

        // Read the OpenCL kernel in from source file.
        std::ifstream file(".\\bin\\Add.cl", std::ifstream::in);
        std::string sourceCode(std::istreambuf_iterator<char>(file), (std::istreambuf_iterator<char>()));

        cl::Program::Sources source(1, std::make_pair(sourceCode.c_str(), sourceCode.length() + 1));

        // Create program.
        cl::Program program = cl::Program(context, source);

        // Build program.
        program.build(devices);

        // Create kernel.
        cl::Kernel kernel(program, "Add");

        // Allocate the OpenCL buffer memory objects for source and result on the device.
        cl::Buffer bufferA = cl::Buffer(context, CL_MEM_READ_ONLY , sizeof(cl_int) * vectorSize);
        cl::Buffer bufferB = cl::Buffer(context, CL_MEM_READ_ONLY , sizeof(cl_int) * vectorSize);
        cl::Buffer bufferC = cl::Buffer(context, CL_MEM_WRITE_ONLY, sizeof(cl_int) * vectorSize);

        // Asynchronous write of data to GPU device.
        queue.enqueueWriteBuffer(bufferA, CL_TRUE, 0, sizeof(cl_int) * vectorSize, a);
        queue.enqueueWriteBuffer(bufferB, CL_TRUE, 0, sizeof(cl_int) * vectorSize, b);

        // Set the Argument values.
        kernel.setArg(0, bufferA);
        kernel.setArg(1, bufferB);
        kernel.setArg(2, bufferC);
        kernel.setArg(3, vectorSize);

        // Launch kernel.
        cl::NDRange global(vectorSize);
        cl::NDRange local(8);

        queue.enqueueNDRangeKernel(kernel, cl::NullRange, global, local);

        // Read back results and check accumulated errors.
        queue.enqueueReadBuffer(bufferC, CL_TRUE, 0, sizeof(cl_int) * vectorSize, c);

        // Print results.
        for (size_t i = 0; i < vectorSize; ++i)
        {
            std::cout << a[i] << " + " << b[i] << " = " << c[i] << std::endl;
        }

        delete[] a;
        delete[] b;
        delete[] c;
    }
    catch (cl::Error error)
    {
        std::cout << error.what() << "(" << error.err() << ")" << std::endl;
    }

    // Press Enter, to quit application.
    std::cin.get();

    return 0;
}

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#define __CL_ENABLE_EXCEPTIONS

#define __NO_STD_VECTOR

#include <iostream>

#include <fstream>

#include <string>

#include <CL/cl.hpp>

void randomizeArray(cl_int* data, size_t vectorSize)

{

for (size_t i = 0; i < vectorSize; ++i)

{

data[i] = rand() % 10;

}

int main()

{

try

{

// Allocate and initialize host arrays

size_t vectorSize = 32;

size_t localWorkSize = 8;

cl_int* a = new cl_int[vectorSize];

cl_int* b = new cl_int[vectorSize];

cl_int* c = new cl_int[vectorSize];

randomizeArray(a, vectorSize);

randomizeArray(b, vectorSize);

// Get platforms.

cl::vector<cl::Platform> platforms;

cl::Platform::get(&platforms);

for (cl::vector<cl::Platform>::iterator i = platforms.begin(); i != platforms.end(); ++i)

{

cl::Platform platform = *i;

std::string name;

platform.getInfo(CL_PLATFORM_NAME, &name);

std::cout << "Name:\t\t" << name << std::endl;

platform.getInfo(CL_PLATFORM_VENDOR, &name);

std::cout << "Vendor:\t\t" << name << std::endl;

std::cout << std::endl;

}

// Create a context.

cl_context_properties cps[3] =

{

CL_CONTEXT_PLATFORM,

(cl_context_properties)(platforms[1])(),

};

cl::Context context( CL_DEVICE_TYPE_GPU, cps);

// Get devices.

cl::vector<cl::Device> devices = context.getInfo<CL_CONTEXT_DEVICES>();

for (cl::vector<cl::Device>::iterator i = devices.begin(); i != devices.end(); ++i)

{

cl::Device device = *i;

std::string name;

device.getInfo(CL_DEVICE_NAME, &name);

std::cout << "Name:\t\t" << name << std::endl;

device.getInfo(CL_DEVICE_VENDOR, &name);

std::cout << "Vendor:\t\t" << name << std::endl;

device.getInfo(CL_DEVICE_VERSION, &name);

std::cout << "Version:\t" << name << std::endl;

std::cout << std::endl;

}

// Create a command queue.

cl::CommandQueue queue = cl::CommandQueue(context, devices[0]);

// Read the OpenCL kernel in from source file.

std::ifstream file(".\\bin\\Add.cl", std::ifstream::in);

std::string sourceCode(std::istreambuf_iterator<char>(file), (std::istreambuf_iterator<char>()));

cl::Program::Sources source(1, std::make_pair(sourceCode.c_str(), sourceCode.length() + 1));

// Create program.

cl::Program program = cl::Program(context, source);

// Build program.

program.build(devices);

// Create kernel.

cl::Kernel kernel(program, "Add");

// Allocate the OpenCL buffer memory objects for source and result on the device.

cl::Buffer bufferA = cl::Buffer(context, CL_MEM_READ_ONLY , sizeof(cl_int) * vectorSize);

cl::Buffer bufferB = cl::Buffer(context, CL_MEM_READ_ONLY , sizeof(cl_int) * vectorSize);

cl::Buffer bufferC = cl::Buffer(context, CL_MEM_WRITE_ONLY, sizeof(cl_int) * vectorSize);

// Asynchronous write of data to GPU device.

queue.enqueueWriteBuffer(bufferA, CL_TRUE, 0, sizeof(cl_int) * vectorSize, a);

queue.enqueueWriteBuffer(bufferB, CL_TRUE, 0, sizeof(cl_int) * vectorSize, b);

// Set the Argument values.

kernel.setArg(0, bufferA);

kernel.setArg(1, bufferB);

kernel.setArg(2, bufferC);

kernel.setArg(3, vectorSize);

// Launch kernel.

cl::NDRange global(vectorSize);

cl::NDRange local(8);

queue.enqueueNDRangeKernel(kernel, cl::NullRange, global, local);

// Read back results and check accumulated errors.

queue.enqueueReadBuffer(bufferC, CL_TRUE, 0, sizeof(cl_int) * vectorSize, c);

// Print results.

for (size_t i = 0; i < vectorSize; ++i)

{

std::cout << a[i] << " + " << b[i] << " = " << c[i] << std::endl;

}

delete[] a;

delete[] b;

delete[] c;

}

catch (cl::Error error)

{

std::cout << error.what() << "(" << error.err() << ")" << std::endl;

}

// Press Enter, to quit application.

std::cin.get();

return 0;

}

Zdefiniowanie „__CL_ENABLE_EXCEPTIONS” przed dołączeniem pliku „cl.hpp” pozwoli na przechwytywanie wyjątków w przypadku wystąpienia błędów.

Zdefiniowanie „__NO_STD_VECTOR” powoduje, że OpenCL używa własnej klasy wektora. Jeżeli usuniemy definicję, to możemy używać wektora z biblioteki STL.

Kod pliku Add.cl

__kernel void Add(__global int* a, __global int* b, __global int* c, int size)
{
    // Find position in global arrays.
    int n = get_global_id(0);

    // Bound check.
    if (n < size)
    {   
        c[n] = a[n] + b[n];
    }
}