OpenCL Matrix Multiplication Using std::vector

Question

I am trying to use OpenCl to preform a vector multiplication, while the code itself seems to work the result returned is either garbage or zeros. From what I can tell it appears that it either the kernel is not receiving the correct values, there is something non-obvious to me that I am missing here, what is it? I thought it was the way I was allocating the buffers but I am uncertain.

#define CL_USE_DEPRECATED_OPENCL_2_0_APIS

#include <iostream>
#include <fstream>
#include <sstream>
#include "./cl.hpp"

void populate_vector(std::vector<float> &vect, std::stringstream &readStream) {

    std::string x;
    std::string fStripped;
    float readFloat;

    while(std::getline(readStream, x, ',')){
        std::stringstream elementStream;
        elementStream << x;
        std::getline(elementStream, fStripped, 'f');
        elementStream << fStripped;
        elementStream >> readFloat;
        vect.push_back(readFloat);
    }

}

int main()
{
    std::vector<cl::Platform> platforms;
    cl::Platform::get(&platforms);
    if(platforms.empty()){
        throw std::runtime_error("No Platforms found, check OpenCL installation.");
    }

    cl::Platform platform = platforms[0];
    std::cout << "Using Platform: " << platform.getInfo<CL_PLATFORM_NAME>() << std::endl;
    std::vector<cl::Device> devices;
    platform.getDevices(CL_DEVICE_TYPE_GPU, &devices);
    if(devices.empty()){
        throw std::runtime_error ("No Devices Found, check installation.");
    }
    cl::Device device = devices[0];

    // Create an execusion context
    cl::Context context(device);

    cl::CommandQueue queue(context,device);


    // Load the kernel sources, use global memory
    std::ifstream fs("mCrossProd.cl");
    if(!fs.is_open()){
        throw  std::runtime_error("Cannot open kernel source file.");
    }

    // Extract kernel code
    std::stringstream ss;
    ss << fs.rdbuf();
    auto code = ss.str();
    cl::Program::Sources sources;
    sources.push_back({code.c_str(), code.length()});
    fs.close();

    // Build the kernel
    cl::Program program(context, sources);
    if(program.build({device})!= CL_SUCCESS){
        std::cout << " Error building: " << program.getBuildInfo<CL_PROGRAM_BUILD_LOG>(device) << "\n";
        exit(1);
    }

    // Output matrix dimensions
    int M = 4, N = 3, K = 6;
    int A_dims = M * K;
    int B_dims = N * K;
    int C_dims = M * N;

    // Create buffers for device
    cl::Buffer buffer_A(context,CL_MEM_READ_WRITE,sizeof(float)*A_dims);
    cl::Buffer buffer_B(context,CL_MEM_READ_WRITE,sizeof(float)*B_dims);
    cl::Buffer buffer_C(context,CL_MEM_READ_WRITE,sizeof(float)*C_dims);

    std::string s;
    std::vector<float> A;
    std::vector<float> B;
    std::vector<float> C;
    std::ifstream infile("matrices.txt");
    std::cout << "Opened file to read" << '\n';


    std::getline(infile,s);
    //std::cout << s;
    std::stringstream mss(s);
    populate_vector(A, mss);   
    std::copy(A.begin(), A.end(), std::ostream_iterator<float>(std::cout, ", "));
    std::cout << '\n';

    mss.str("");
    mss.clear();
    std::getline(infile,s);
    mss << s;
    populate_vector(B, mss);  
    std::copy(B.begin(), B.end(), std::ostream_iterator<float>(std::cout, ", "));
    std::cout << '\n';

    mss.str("");
    mss.clear();
    std::getline(infile,s);
    mss << s;
    populate_vector(C, mss);
    std::copy(C.begin(), C.end(), std::ostream_iterator<float>(std::cout, ", "));
    std::cout << '\n';




    //write arrays A and B to the device
    queue.enqueueWriteBuffer(buffer_A,CL_TRUE,0,A.size()*sizeof(float),&A);
    queue.enqueueWriteBuffer(buffer_B,CL_TRUE,0,B.size()*sizeof(float),&B);

    std::cout << A.size() * sizeof(float) << '\n';
    std::cout << B.size() * sizeof(float) << '\n';
    std::cout << C.size() * sizeof(float) << '\n';

    // Select kernel, pass arguments
    cl::Kernel kernel = cl::Kernel(program, "mCrossProd");
    kernel.setArg(0, M);
    kernel.setArg(1, N);
    kernel.setArg(2, K);
    kernel.setArg(3, buffer_A);
    kernel.setArg(4, buffer_B);
    kernel.setArg(5, buffer_C);

    // Execute kernel
    if( queue.enqueueNDRangeKernel(kernel,cl::NullRange,cl::NDRange(M,N),cl::NDRange(1,1)) != CL_SUCCESS )
    {
        std::cout << "Failed to launch kernel" << std::endl;
        exit(1);
    }
    queue.finish();

    // read result C from the device to array C
    queue.enqueueReadBuffer(buffer_C,CL_TRUE,0,C.size(),&C[0]);
    std::cout << C.size() << std::endl;
    std::cout << C_dims << std::endl;
    std::cout << M << " " << N << std::endl;
    std::cout << "\nThe solution is" << std::endl;
    std::copy(C.begin(), C.end(), std::ostream_iterator<float>(std::cout, ", "));
    std::cout << '\n';

     for(int i = 0; i < M; i++) {
        for(int j = 0; j < N; j++) {
            std::cout << "C[" + std::to_string(i*N+j) + "] = ";
            std::cout << C[i*N+j] << " ";
        }
        std::cout << std::endl;
    }
}

The test Kernel

    __kernel void mCrossProd(const int M, const int N, const int K, __global float* A, __global float* B, __global float* C) {
    int const i = get_global_id(0);
    int const j = get_global_id(1);
    int const debug_elem_id = 3; // purely for debug purposes.

    for(int k = 0; k < K; k++){
        C[i*N+j] += A[i*K+k] * B[N*k+j];
        if((i*N+j)==debug_elem_id)
        {   
            //printf("PROD, i = %d, j = %d, k = %d, N = %d\n", i,j,k,N);
            printf("PROD, %.2f\n", A[i*K+k] * B[N*k+j]);
            printf("SUM: %.2f\n", C[i*N+j]);
        }
    }
}

The contents of matrices.txt

1.5f, 1.0f, 2.0f, 2.0f, 4.0f, 1.0f, 4.0f, 2.0f, 1.0f, 1.0f, 0.0f, 0.0f, 3.0f, 2.0f, 5.0f, 1.0f, 1.0f, 1.0f, 0.0f, 0.0f, 0.0f, 2.0f, 1.0f, 1.0f
1.5f, 2.0f, 4.0f, 1.0f, 1.0f, 2.0f, 4.0f, 2.0f, 1.0f, 0.0f, 0.0f, 1.0f, 9.0f, 2.0f, 1.0f, 2.0f, 1.0f, 0.0f
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f

Answer 1

    queue.enqueueWriteBuffer(buffer_A,CL_TRUE,0,A.size()*sizeof(float),&A);
    queue.enqueueWriteBuffer(buffer_B,CL_TRUE,0,B.size()*sizeof(float),&B);

&A should be either A.data() or &A[0] , I recommend the first one