CUDA Matrix Transpose shared Memory

#include "cuda_runtime.h"
#include "device_functions.h"
#include "device_launch_parameters.h"

#include <chrono>
#include <time.h>
#include <stdio.h>
#include <stdlib.h>

#define TILE_DIM 32
#define BLOCK_ROWS 8
#define BLOCK_COLS 32

cudaError_t matrMagicCuda(float *matrB, float *matrA, const int width, const int height, const int nreps, const int operation);
void cpuMatrTrans(float *matrB, float *matrA, const int width, const int height, const int nreps);
__global__ void naiveTransKernel(float *matrB, float *matrA, const int width, const int height, const int nreps);
__global__ void notSoNaivaTransKernel(float *matrB, float *matrA, const int width, const int height, const int nreps);

int main()
{
    int i, width, height, nreps, size, wrong, correct;
    double cpuTime, cpuBandwidth;
    cudaError_t cudaStatus;

    float *matrA, *matrATC, *matrATG, *matrAC;

    srand(time(NULL));

    nreps = 10000;
    width = 500;
    height = 100;
    size = width * height;

    matrA = (float*)malloc(size * sizeof(float)); // matrix A
    matrAC = (float*)malloc(size * sizeof(float)); // matrix A copied
    matrATC = (float*)malloc(size * sizeof(float)); // matrix A transposed by CPU
    matrATG = (float*)malloc(size * sizeof(float)); // matrix A transposed by GPU

    for (i = 0; i < size; i++)
    {
        matrA[i] = (float)i;
    }

    auto start = std::chrono::high_resolution_clock::now();

    //CPU Transpose
    cpuMatrTrans(matrATC, matrA, width, height, nreps);

    auto end = std::chrono::high_resolution_clock::now();

    std::chrono::duration<double> diff = end - start;
    cpuTime = (diff.count() * 1000) / nreps;
    cpuBandwidth = (sizeof(float) * size * 2) / (cpuTime * 1000000);//scaling from ms to s and B to GB doen implicitly, shortened in fraction, times two for read and write
    printf("Avg. CPU Transpose Time: %f ms, Bandwidth: %f GB/s\n\n", cpuTime, cpuBandwidth);

    correct = 0;
    wrong = 0;

    //Naive transpose
    matrMagicCuda(matrATG, matrA, width, height, nreps, 1);

    //Check if calc was correct
    for (i = 0; i < size; i++)
    {
        if (matrATC[i] != matrATG[i])
        {
            /*printf("ERROR - %d - ATC:%f - ATG:%f\n\n", i, matrATC[i], matrATG[i]);
            return;*/
            wrong++;
        }
        else
        {
            correct++;
        }
    }

    printf("\tCorrect: %d, Wrong: %d\n\n", correct, wrong);
    correct = 0;
    wrong = 0;

    //Transpose with shared memory
    matrMagicCuda(matrATG, matrA, width, height, nreps, 2);

    //Check if calc was correct
    for (i = 0; i < size; i++)
    {
        if (matrATC[i] != matrATG[i])
        {
            /*printf("ERROR - %d - ATC:%f - ATG:%f\n\n", i, matrATC[i], matrATG[i]);
            return;*/
            wrong++;
        }
        else
        {
            correct++;
        }
    }

    //printf("\tTranspose with SM on GPU was executed correctly.\n\n");
    printf("\tCorrect: %d, Wrong: %d\n\n", correct, wrong);
    correct = 0;
    wrong = 0;

    // cudaDeviceReset must be called before exiting in order for profiling and
    // tracing tools such as Nsight and Visual Profiler to show complete traces.
    cudaStatus = cudaDeviceReset();
    if (cudaStatus != cudaSuccess)
    {
        fprintf(stderr, "cudaDeviceReset failed!\n");
        return 1;
    }

    return 0;
}

cudaError_t matrMagicCuda(float *matrB, float *matrA, const int width, const int height, const int nreps, const int operation)
{
    float elapsed = 0;
    float *dev_matrA = 0;
    float *dev_matrB = 0;
    cudaError_t cudaStatus;
    dim3 dim_grid, dim_block;
    double gpuBandwidth;

    int size = width * height;

    dim_block.x = TILE_DIM;
    dim_block.y = BLOCK_ROWS;
    dim_block.z = 1;

    dim_grid.x = (width + TILE_DIM - 1) / TILE_DIM;
    dim_grid.y = (height + TILE_DIM - 1) / TILE_DIM;
    dim_grid.z = 1;

    // Choose which GPU to run on, change this on a multi-GPU system.
    cudaStatus = cudaSetDevice(0);
    if (cudaStatus != cudaSuccess)
    {
        fprintf(stderr, "cudaSetDevice failed!  Do you have a CUDA-capable GPU installed?");
        goto Error;
    }

    // Allocate GPU buffers for three matrix
    cudaStatus = cudaMalloc((void**)&dev_matrA, size * sizeof(float));
    if (cudaStatus != cudaSuccess)
    {
        fprintf(stderr, "cudaMalloc failed!");
        goto Error;
    }

    cudaStatus = cudaMalloc((void**)&dev_matrB, size * sizeof(float));
    if (cudaStatus != cudaSuccess)
    {
        fprintf(stderr, "cudaMalloc failed!");
        goto Error;
    }

    // Copy input matrix from host memory to GPU buffers.
    cudaStatus = cudaMemcpy(dev_matrA, matrA, size * sizeof(float), cudaMemcpyHostToDevice);
    if (cudaStatus != cudaSuccess)
    {
        fprintf(stderr, "cudaMemcpy failed!");
        goto Error;
    }

    cudaEvent_t start, stop;
    cudaEventCreate(&start);
    cudaEventCreate(&stop);

    switch (operation)
    {
        case(1):
        {
            cudaEventRecord(start);
            // Launch a kernel on the GPU with one thread for each element.
            naiveTransKernel << <dim_grid, dim_block >> >(dev_matrB, dev_matrA, width, height, nreps);

            cudaEventRecord(stop);
            cudaEventSynchronize(stop);

            cudaEventElapsedTime(&elapsed, start, stop);
            cudaEventDestroy(start);
            cudaEventDestroy(stop);

            elapsed /= nreps;

            gpuBandwidth = (sizeof(float) * size * 2) / (elapsed * 1000000);//scaling from ms to s and B to GB doen implicitly, shortened in fraction, times two for read and write
            printf("Avg. GPU Naive Trans Time: %f ms, bandwidth: %f GB/s\n", elapsed, gpuBandwidth);

            break;
        }

        case(2):
        {
            cudaEventRecord(start);
            // Launch a kernel on the GPU with one thread for each element.
            notSoNaivaTransKernel << <dim_grid, dim_block >> >(dev_matrB, dev_matrA, width, height, nreps);

            cudaEventRecord(stop);
            cudaEventSynchronize(stop);

            cudaEventElapsedTime(&elapsed, start, stop);
            cudaEventDestroy(start);
            cudaEventDestroy(stop);

            elapsed /= nreps;

            gpuBandwidth = (sizeof(float) * size * 2) / (elapsed * 1000000);//scaling from ms to s and B to GB doen implicitly, shortened in fraction, times two for read and write
            printf("Avg. GPU Trans with SM Time: %f ms, bandwidth: %f GB/s\n", elapsed, gpuBandwidth);

            break;
        }

    default:
        printf("No matching opcode was found.\n");
    }

    // Check for any errors launching the kernel
    cudaStatus = cudaGetLastError();
    if (cudaStatus != cudaSuccess)
    {
        fprintf(stderr, "Kernel launch failed: %s\n", cudaGetErrorString(cudaStatus));
        goto Error;
    }

    // cudaDeviceSynchronize waits for the kernel to finish, and returns
    // any errors encountered during the launch.
    cudaStatus = cudaDeviceSynchronize();
    if (cudaStatus != cudaSuccess)
    {
        fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching Kernel!\n", cudaStatus);
        goto Error;
    }

    // Copy output matrix from GPU buffer to host memory.
    cudaStatus = cudaMemcpy(matrB, dev_matrB, size * sizeof(float), cudaMemcpyDeviceToHost);
    if (cudaStatus != cudaSuccess)
    {
        fprintf(stderr, "cudaMemcpy failed!");
        goto Error;
    }

Error:
    cudaFree(dev_matrB);
    cudaFree(dev_matrA);

    return cudaStatus;
}

void cpuMatrTrans(float *matrB, float *matrA, const int width, const int height, const int nreps)
{
    int i, j, r;

#pragma unroll
    for (r = 0; r < nreps; r++)
#pragma unroll
        for (i = 0; i < height; i++)
#pragma unroll
            for (j = 0; j < width; j++)
                matrB[j * height + i] = matrA[i * width + j];
}

__global__ void naiveTransKernel(float *matrB, float *matrA, const int width, const int height, const int nreps)
{
    int i, r;
    int row = blockIdx.x * TILE_DIM + threadIdx.x;
    int col = blockIdx.y * TILE_DIM + threadIdx.y;
    int index_in = row + width * col;
    int index_out = col + height * row;

#pragma unroll
    for (r = 0; r < nreps; r++)
#pragma unroll
        for (i = 0; i < TILE_DIM; i += BLOCK_ROWS)
            if (index_in + i * width < width * height)
                matrB[index_out + i] = matrA[index_in + i * width];
}

__global__ void notSoNaivaTransKernel(float *matrB, float *matrA, const int width, const int height, const int nreps)
{
    __shared__ float tile[TILE_DIM][TILE_DIM + 1];
    int blockIdx_y = blockIdx.x;
    int blockIdx_x = (blockIdx.x + blockIdx.y) % gridDim.x;
    int xIndex = blockIdx_x * TILE_DIM + threadIdx.x;
    int yIndex = blockIdx_y * TILE_DIM + threadIdx.y;
    int index_in = xIndex + (yIndex)* width;

    xIndex = blockIdx_y * TILE_DIM + threadIdx.x;
    yIndex = blockIdx_x * TILE_DIM + threadIdx.y;
    int index_out = xIndex + (yIndex)* height;

    int r, i;
#pragma unroll
    for (r = 0; r < nreps; r++)
    {
#pragma unroll
        for (i = 0; i < TILE_DIM; i += BLOCK_ROWS)
            tile[threadIdx.y + i][threadIdx.x] = matrA[index_in + i * width];

        __syncthreads();

#pragma unroll
        for (i = 0; i < TILE_DIM; i += BLOCK_ROWS)
            if (index_in + i * width < width * height)
               matrB[index_out + i * height] = tile[threadIdx.x][threadIdx.y + i];
    }
}