1
Fork 0
mirror of https://github.com/Steffo99/unimore-hpc-assignments.git synced 2024-11-22 16:14:24 +00:00
hpc-2022-g3/cuda/lab2/exercise3.cu
Alessandro Capotondi 809d881f2f HPC CUDA Lab 2
2021-05-04 10:12:31 +02:00

179 lines
5.3 KiB
Text

/*
* BSD 2-Clause License
*
* Copyright (c) 2020, Alessandro Capotondi
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* * Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file exercise3.cu
* @author Alessandro Capotondi
* @date 5 May 2020
* @brief Exercise 3 - Image Luminance Histogram
*
* @see https://dolly.fim.unimore.it/2019/course/view.php?id=152
*/
#include <stdio.h>
#include <time.h>
#include <sys/time.h>
#include <opencv2/opencv.hpp>
#include <opencv2/imgcodecs/imgcodecs.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include <opencv2/highgui/highgui.hpp>
using namespace cv;
using namespace std;
#ifndef BLOCK_SIZE
#define BLOCK_SIZE 32
#endif
#define gpuErrchk(ans) \
{ \
gpuAssert((ans), __FILE__, __LINE__); \
}
static inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort = true)
{
if (code != cudaSuccess)
{
fprintf(stderr, "GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort)
exit(code);
}
}
extern "C"
{
#include "utils.h"
}
#define NBINS 256
void hist(unsigned char *__restrict__ im, int *__restrict__ hist, int width, int height)
{
#pragma omp parallel for
for (int i = 0; i < width * height; i++)
{
int val = im[i];
#pragma omp atomic
hist[val]++;
}
}
//TODO Ex3-a) Implement Histogram Calculation. Using Global Accesses
__global__ void hist_v1(unsigned char *__restrict__ im, int *__restrict__ hist, int width, int height)
{
}
//TODO Ex3-b) Implement Histogram Calculation. Exploiting Shared Memory
__global__ void hist_v2(unsigned char *__restrict__ im, int *__restrict__ hist, int width, int height)
{
}
int main(int argc, char *argv[])
{
int iret = 0;
struct timespec rt[2];
double wt; // walltime
int hist_host[NBINS], hist_gpu[NBINS];
string filename("data/buzz.jpg");
if (argc > 1)
filename = argv[1];
// Load Image
Mat image = imread(filename, IMREAD_GRAYSCALE);
if (!image.data)
{
cout << "Could not open or find the image" << std::endl;
return -1;
}
int width = image.size().width;
int height = image.size().height;
// Set Output Memory
memset(hist_host, 0, NBINS * sizeof(int));
memset(hist_gpu, 0, NBINS * sizeof(int));
// Compute CPU Version - Golden Model
clock_gettime(CLOCK_REALTIME, rt + 0);
hist(image.ptr(), hist_host, width, height);
clock_gettime(CLOCK_REALTIME, rt + 1);
wt = (rt[1].tv_sec - rt[0].tv_sec) + 1.0e-9 * (rt[1].tv_nsec - rt[0].tv_nsec);
printf("Hist (Host) : %9.6f sec\n", wt);
//CUDA Buffer Allocation
int *d_hist_gpu;
unsigned char *d_image;
gpuErrchk(cudaMalloc((void **)&d_hist_gpu, sizeof(int) * NBINS));
gpuErrchk(cudaMalloc((void **)&d_image, sizeof(unsigned char) * width * height));
clock_gettime(CLOCK_REALTIME, rt + 0);
//TODO Copy Image to the device
//TODO Define Grid and Block
//TODO Launch Kernel hist_v1
gpuErrchk(cudaPeekAtLastError());
//TODO Copy histogram from the device
clock_gettime(CLOCK_REALTIME, rt + 1);
wt = (rt[1].tv_sec - rt[0].tv_sec) + 1.0e-9 * (rt[1].tv_nsec - rt[0].tv_nsec);
printf("Hist (GPU) : %9.6f sec\n", wt);
for (int i = 0; i < NBINS; i++)
{
iret = *(int *)(hist_host + i) ^ *(int *)(hist_gpu + i);
assert(iret == 0);
}
// Reset Output
gpuErrchk(cudaMemset(d_hist_gpu, 0, NBINS * sizeof(unsigned int)));
clock_gettime(CLOCK_REALTIME, rt + 0);
//TODO Copy Image to the device
//Use the same dimBlock, dimGrid of previous version
//TODO Launch Kernel hist_v2
gpuErrchk(cudaPeekAtLastError());
//TODO Copy histogram from the device
clock_gettime(CLOCK_REALTIME, rt + 1);
wt = (rt[1].tv_sec - rt[0].tv_sec) + 1.0e-9 * (rt[1].tv_nsec - rt[0].tv_nsec);
printf("Hist-2 (GPU) : %9.6f sec\n", wt);
for (int i = 0; i < NBINS; i++)
{
iret = *(int *)(hist_host + i) ^ *(int *)(hist_gpu + i);
assert(iret == 0);
}
gpuErrchk(cudaFree(d_hist_gpu));
gpuErrchk(cudaFree(d_image));
return iret;
}