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/exercise1.cu
Alessandro Capotondi 809d881f2f HPC CUDA Lab 2
2021-05-04 10:12:31 +02:00

137 lines
4 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 exercise1.cu
* @author Alessandro Capotondi
* @date 27 Mar 2020
* @brief Exercise 1
*
* @see https://dolly.fim.unimore.it/2019/course/view.php?id=152
*/
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <cuda_runtime.h>
#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 TWO02 (1 << 2)
#define TWO04 (1 << 4)
#define TWO08 (1 << 8)
#ifndef N
#define N (1LL << 28)
#endif
#ifndef BLOCK_SIZE
#define BLOCK_SIZE (1024)
#endif
/**
* @brief EX 1 - Offset and Strided Accesses
*
* a) Measure the bandwidth accessing the memory using an offset = {1,2,4,8,16,32} (mem_update v1)
* b) Measure the bandwidth accessing the memory using a stride = {1,2,4,8,16,32} (mem_update v2)
*
* @return void
*/
#ifndef STRIDE
#define STRIDE 0
#endif
// mem_update v1 - Offseted Accesses
__global__ void mem_udpate(float * __restrict__ y, float a)
{
int i = threadIdx.x + blockIdx.x * blockDim.x;
y[(i+STRIDE)%N] = a;
}
// mem_update v2 - Strided Accesses
// __global__ void mem_udpate(float * __restrict__ y, float a)
// {
// int i = threadIdx.x + blockIdx.x * blockDim.x;
// y[(i*STRIDE)%N] = a;
// }
int main(int argc, const char **argv)
{
int iret = 0;
float *h_y, *d_y;
float a = 101.0f / TWO02;
if (NULL == (h_y = (float *)malloc(sizeof(float) * N)))
{
printf("error: memory allocation for 'y'\n");
iret = -1;
}
if (0 != iret)
{
free(h_y);
exit(EXIT_FAILURE);
}
//CUDA Buffer Allocation
gpuErrchk(cudaMalloc((void **)&d_y, sizeof(float) * N));
gpuErrchk(cudaMemcpy(d_y, h_y, sizeof(float) * N, cudaMemcpyHostToDevice));
start_timer();
mem_udpate<<<128*BLOCK_SIZE,BLOCK_SIZE>>>(d_y, a);
gpuErrchk(cudaPeekAtLastError());
cudaDeviceSynchronize();
stop_timer();
gpuErrchk(cudaMemcpy(h_y, d_y, sizeof(float) * N, cudaMemcpyDeviceToHost));
printf("mem_udpate (GPU): %9.3f sec %9.1f MB/s\n", elapsed_ns() / 1.0e9, (4 * 128*BLOCK_SIZE*BLOCK_SIZE) / ((1.0e6 / 1e9) * elapsed_ns()));
//CUDA Buffer Allocation
free(h_y);
gpuErrchk(cudaFree(d_y));
// CUDA exit -- needed to flush printf write buffer
cudaDeviceReset();
return 0;
}