notes.txt

Relevant steps for initialization

    // Step 1: Platform and Device Setup
    cl_platform_id platform;
    cl_device_id device;
    cl_uint num_platforms, num_devices;
    
    // Get platform
    clGetPlatformIDs(1, &platform, &num_platforms);
    
    // Get device
    clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, 1, &device, &num_devices);
    
    // Step 2: Create Context and Command Queue
    cl_context context = clCreateContext(NULL, 1, &device, NULL, NULL, NULL);
    cl_command_queue queue = clCreateCommandQueue(context, device, 0, NULL);
    
    // Step 3: Create and Build Program
    cl_program program = clCreateProgramWithSource(context, 1, 
                                                 &matrixMultKernel, NULL, NULL);
    clBuildProgram(program, 1, &device, NULL, NULL, NULL);
    
    // Step 4: Create Kernel
    cl_kernel kernel = clCreateKernel(program, "matrixMult", NULL);
    
    // Step 5: Prepare Data
    const int M = 1024;  // Matrix dimensions
    const int N = 1024;
    const int K = 1024;
    
    size_t matrix_a_size = M * N * sizeof(float);
    size_t matrix_b_size = N * K * sizeof(float);
    size_t matrix_c_size = M * K * sizeof(float);
    
    float* matrix_a = (float*)malloc(matrix_a_size);
    float* matrix_b = (float*)malloc(matrix_b_size);
    float* matrix_c = (float*)malloc(matrix_c_size);
    
    // Initialize matrices (example initialization)
    for (int i = 0; i < M * N; i++) matrix_a[i] = 1.0f;
    for (int i = 0; i < N * K; i++) matrix_b[i] = 1.0f;
    
    // Step 6: Create Buffer Objects
    cl_mem buffer_a = clCreateBuffer(context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
                                   matrix_a_size, matrix_a, NULL);
    cl_mem buffer_b = clCreateBuffer(context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
                                   matrix_b_size, matrix_b, NULL);
    cl_mem buffer_c = clCreateBuffer(context, CL_MEM_WRITE_ONLY,
                                   matrix_c_size, NULL, NULL);
    
    // Step 7: Set Kernel Arguments
    clSetKernelArg(kernel, 0, sizeof(cl_mem), &buffer_a);
    clSetKernelArg(kernel, 1, sizeof(cl_mem), &buffer_b);
    clSetKernelArg(kernel, 2, sizeof(cl_mem), &buffer_c);
    clSetKernelArg(kernel, 3, sizeof(int), &M);
    clSetKernelArg(kernel, 4, sizeof(int), &N);
    clSetKernelArg(kernel, 5, sizeof(int), &K);
    
    // Step 8: Execute Kernel
    size_t global_work_size[2] = {M, K};
    clEnqueueNDRangeKernel(queue, kernel, 2, NULL, global_work_size, 
                          NULL, 0, NULL, NULL);
    
    // Step 9: Read Results
    clEnqueueReadBuffer(queue, buffer_c, CL_TRUE, 0, 
                       matrix_c_size, matrix_c, 0, NULL, NULL);
    
    // Step 10: Cleanup
    clReleaseMemObject(buffer_a);
    clReleaseMemObject(buffer_b);
    clReleaseMemObject(buffer_c);
    clReleaseKernel(kernel);
    clReleaseProgram(program);
    clReleaseCommandQueue(queue);
    clReleaseContext(context);
    
    free(matrix_a);
    free(matrix_b);
    free(matrix_c);
    
    return 0;


Recommendations for optimization

1. Use local memory for frequently accessed data
2. Ensure proper memory alignment
3. Choose appropriate work-group sizes
4. Use vector types when possible
5. Minimize branching in kernels
6. Consider memory coalescing
7. Use async operations when possible */

// Example of optimized kernel using local memory
const char* optimizedKernel =
"__kernel void matrixMultOptimized(__global float* A,\n"
"                                 __global float* B,\n"
"                                 __global float* C,\n"
"                                 const int M, const int N, const int K,\n"
"                                 __local float* localA,\n"
"                                 __local float* localB) {\n"
"    const int TILE_SIZE = 16;\n"
"    \n"
"    int row = get_global_id(0);\n"
"    int col = get_global_id(1);\n"
"    int localRow = get_local_id(0);\n"
"    int localCol = get_local_id(1);\n"
"    \n"
"    float sum = 0.0f;\n"
"    \n"
"    for (int t = 0; t < N; t += TILE_SIZE) {\n"
"        // Load tile into local memory\n"
"        localA[localRow * TILE_SIZE + localCol] = \n"
"            A[row * N + t + localCol];\n"
"        localB[localRow * TILE_SIZE + localCol] = \n"
"            B[(t + localRow) * K + col];\n"
"        \n"
"        barrier(CLK_LOCAL_MEM_FENCE);\n"
"        \n"
"        // Compute on tile\n"
"        for (int k = 0; k < TILE_SIZE; k++) {\n"
"            sum += localA[localRow * TILE_SIZE + k] *\n"
"                   localB[k * TILE_SIZE + localCol];\n"
"        }\n"
"        \n"
"        barrier(CLK_LOCAL_MEM_FENCE);\n"
"    }\n"
"    \n"
"    if (row < M && col < K) {\n"
"        C[row * K + col] = sum;\n"
"    }\n"
"}\n";