【Cuda编程】加法归约

cuda编程并行归约

目录

AtomicAdd调用出错

在cuda中调用atomicAdd函数，但总显示未定义标识符，在网上送了一下，于是做了如下修改，

右键解决方案属性-》配置属性-》CUDA C/C++-》Device-》Code Generation，加入compute_20,sm_20，并且把下面的“从父级或项目属性默认设置继承”的勾选去掉

gpu cpu下时间计算

//cpu 下
#include <time.h>
clock_t start,end;
start  = clock();
//cpu codes
end = clock();
printf("CPU Time: %.5f
", (float)(end-start));

//gpu 下
cudaEvent_t st,ed;
cudaEventCreate(&st);
cudaEventCreate(&ed);
cudaEventRecord(st,0);
//gpu codes
cudaEventRecord(ed,0);
cudaEventSynchronize(ed);
float gpu_time;
cudaEventElapsedTime(&gpu_time,st,ed);
printf("GPU Time: %.5f
",gpu_time);

cudaEventDestroy(st);
cudaEventDestroy(ed);

加法的归约

#include <stdlib.h>
#include <stdio.h>
#include <cuda.h>
#include <device_launch_parameters.h>
#include <cuda_runtime.h>
#include <book.h>

const int Size = 256;
const int block = 8;
const int thread = 32;

__global__ void calc(float *in, float *out){
	unsigned int tid = threadIdx.x;
	unsigned int bid = blockIdx.x;

	//target array
	float * target = in + blockIdx.x * blockDim.x;

	//bounding
	if(tid > thread)
		return;

	for(int stride = 1 ; stride < blockDim.x ; stride *= 2)
	{
		if(tid % (stride*2) == 0)
		{
			target[tid] += target[tid+stride];
		}
		__syncthreads();
	}

	if(tid == 0)
	{
		out[blockIdx.x] = target[tid];
	}
}

__global__ void calc2(float *in, float *out)
{
	unsigned int tid = threadIdx.x;
	unsigned int bid = tid + blockIdx.x*blockDim.x;

	float * target = in + blockIdx.x * blockDim.x;

	//bounding
	if(tid > thread)
		return;
	//stride = 1,2,4,8
	for(int stride = 1 ; stride < blockDim.x ; stride *= 2)
	{
		unsigned int index = 2*stride*tid;
		if(index < blockDim.x)
			target[index] += target[index+stride];
		__syncthreads();
	}

	if(tid == 0)
	{
		out[blockIdx.x] = target[tid];
	}
}

//跨步规约
__global__ void calc3(float *in, float *out)
{
	unsigned int tid = threadIdx.x;
	unsigned int bid = tid + blockIdx.x*blockDim.x;

	float * target = in + blockIdx.x * blockDim.x;

	//bounding
	if(tid > thread)
		return;

	for(int stride = blockDim.x/2 ; stride > 0 ; stride /=2)
	{
		if(tid < stride)
			target[tid] += target[tid+stride];
		__syncthreads();
	}
	if(tid == 0)
	{
		out[blockIdx.x] = target[tid];
	}
} 

__global__ void calc4(float *in, float *out)
{
	int tid = threadIdx.x;
	int bid = blockIdx.x;
	
	float * target=in + bid * blockDim.x;

	if(tid < thread)
		return;
	__shared__ float share_in[thread];

	share_in[tid] = target[tid];

	__syncthreads();

	for(int stride = blockDim.x/2 ; stride > 0; stride /= 2)
	{
		if(tid < stride)
		{
			share_in[tid] += share_in[tid+stride];
		}
		__syncthreads();
	}
	if(tid == 0)
	{
		out[blockIdx.x] = share_in[tid];
	}
}

int main()
{
	//host
	float * indata; // Size
	float * outdata; // block
	float * ans; // 1

	// device
	float * dev_indata; // Size
	float * dev_outdata; // block

	// host malloc
	indata = (float*)malloc(sizeof(float)*Size);
	outdata = (float*)malloc(sizeof(float)*block);
	ans = (float*)malloc(sizeof(float));

	// device malloc
	cudaMalloc((void**)&dev_indata,sizeof(float)*Size);
	cudaMalloc((void**)&dev_outdata,sizeof(float)*block);

	// init & generate data
	for(int i = 0 ; i < Size ; i++)
	{
		indata[i] = i;
	}
	*ans = 0;

	// time start
	cudaEvent_t st,ed;
	cudaEventCreate(&st);
	cudaEventCreate(&ed);
	cudaEventRecord(st,0);

	// memcpy to device
	HANDLE_ERROR(cudaMemcpy(dev_indata,indata,sizeof(float)*Size,cudaMemcpyHostToDevice));

	// kernal functions
	cudaDeviceSynchronize();
	calc4<<<block,thread>>>(dev_indata,dev_outdata);
	cudaDeviceSynchronize();

	// memcpy to host
	HANDLE_ERROR(cudaMemcpy(outdata,dev_outdata,sizeof(float)*block,cudaMemcpyDeviceToHost));

	// time end
	cudaEventRecord(ed,0);
	cudaEventSynchronize(ed);

	float gpu_time;
	cudaEventElapsedTime(&gpu_time,st,ed);
	
	// test output
	for(int i = 0 ; i < block ; i++)
	{
		//printf("%.3f
",outdata[i]);
		*ans += outdata[i];
	}
	printf("GPU Time: %.5f
Ans: %.5f
",gpu_time,*ans);

	//time destory
	cudaEventDestroy(st);
	cudaEventDestroy(ed);

	//device destory
	cudaFree(indata);
	cudaFree(outdata);
	cudaFree(ans);

	getchar();

	return 0;
}

矩阵乘法

#include <stdlib.h>
#include <cuda_runtime.h>
#include <stdio.h>
#include <cuda.h>
#include <device_launch_parameters.h>

const int N = 20;

__global__ void mul(int *a,int* b,int *out)
{
  unsigned int tidx = threadIdx.x;
  unsigned int tidy = threadIdx.y;

  unsigned int offset = tidx*N + tidy;

  if(offset > N*N)return;

  int t = 0;
  for(int i = 0 ; i < N ; i++)
  {
    t += a[tidx*N+i]*b[i*N+tidy];
  }
  out[offset] = t;
}

int main()
{
  //host
  int * matrix1;
  int * matrix2;
  int * output;

  //device
  int * dev_matrix1;
  int * dev_matrix2;
  int * dev_output;

  //host malloc
  matrix1 = (int*)malloc(sizeof(int)*N*N);
  matrix2 = (int*)malloc(sizeof(int)*N*N);
  output = (int*)malloc(sizeof(int)*N*N);

  //device malloc
  cudaMalloc((void**)&dev_matrix1,sizeof(int)*N*N);
  cudaMalloc((void**)&dev_matrix2,sizeof(int)*N*N);
  cudaMalloc((void**)&dev_output,sizeof(int)*N*N);

  //init generate data
  for(int i = 0 ; i < N*N ; i++)
  {
    matrix1[i] = i+1;
    matrix2[i] = i+1;
    output[i] = 0;
  }

  //CPU
  for(int i = 0 ; i < N ; i++)
  {
    for(int j = 0 ; j < N ; j++){
      int tp = 0;
      for(int k = 0 ; k < N ; k++)
      {
        tp += matrix1[i*N+k] * matrix2[k*N+j];
      }
      printf("%d ",tp);
    }
  }
  printf("
----------
");

  //time start
  cudaEvent_t st,ed;
  cudaEventCreate(&st);
  cudaEventCreate(&ed);
  cudaEventRecord(st,0);

  //memcpy to device
  cudaMemcpy(dev_matrix1,matrix1,sizeof(int)*N*N,cudaMemcpyHostToDevice);
  cudaMemcpy(dev_matrix2,matrix2,sizeof(int)*N*N,cudaMemcpyHostToDevice);

  //kernel functions
  mul<<<2,dim3(N,N)>>>(dev_matrix1,dev_matrix2,dev_output);

  //memcpy to host
  cudaMemcpy(output,dev_output,sizeof(int)*N*N,cudaMemcpyDeviceToHost);

  //output
  for(int i = 0 ; i < N*N ; i++)
  {
    printf("%d ",output[i]);
  }
  printf("
");

  //time end
  cudaEventRecord(ed,0);
  cudaEventSynchronize(ed);
  float gpu_time;
  cudaEventElapsedTime(&gpu_time,st,ed);
  printf("gpu time: %.5f
",gpu_time);

  //time destory
  cudaEventDestroy(st);
  cudaEventDestroy(ed);

  //device destory
  cudaFree(dev_matrix1);
  cudaFree(dev_matrix2);
  cudaFree(dev_output);
  free(matrix1);
  free(matrix2);
  free(output);

  return 0;
}

矩阵转置

#include <iostream>
#include <stdlib.h>
#include <stdio.h>
#include "cuda.h"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"

const int N = 5;

void output(int * arr)
{
  for(int i = 0 ; i < N*N ; i++)
  {
    printf("%d	",arr[i]);
    if((i+1) % N == 0)
      printf("
");
  }
  printf("
");
}

__global__ void trans(int * in, int * out)
{

  unsigned int xIndex = threadIdx.x + blockDim.x * blockIdx.x;
  unsigned int yIndex = threadIdx.y + blockDim.y * blockIdx.y;

  if(xIndex < N && yIndex < N)
  {
    unsigned int index_in = xIndex + N * yIndex;
    unsigned int index_out = yIndex + N * xIndex;
    out[index_out] = in[index_in];
  }
}

__global__ void trans2(int * in , int * out)
{
  __shared__ float block[N][N];
  unsigned int xIndex = blockIdx.x * N + threadIdx.x;
  unsigned int yIndex = blockIdx.y * N + threadIdx.y;
  if((xIndex < N) && (yIndex < N))
  {
    unsigned int index_in = yIndex * N +xIndex;
    block[threadIdx.x][threadIdx.y] = in[index_in];
  }

  __syncthreads();

  xIndex = blockIdx.y * N + threadIdx.x;
  yIndex = blockIdx.x * N + threadIdx.y;
  if((xIndex < N) && (yIndex < N))
  {
    unsigned int index_out = yIndex * N + xIndex;
    out[index_out] = block[threadIdx.x][threadIdx.y];
  }
}


int main()
{
  //host
  int * in;
  int * out;

  //device
  int * dev_in;    
  int * dev_out;

  //host cudaMalloc
  in = (int*)malloc(sizeof(int)*N*N);
  out = (int*)malloc(sizeof(int)*N*N);

  //device cudaMalloc
  cudaMalloc((void**)&dev_in,sizeof(int)*N*N);
  cudaMalloc((void**)&dev_out,sizeof(int)*N*N);

  //init
  for(int i = 0 ; i < N*N ; i++){
    in[i] = i+1;
  }

  //cudaMemcpy
  cudaMemcpy(dev_in,in,sizeof(int)*N*N,cudaMemcpyHostToDevice);

  //kernel functions
  trans<<<1,dim3(N,N)>>>(dev_in,dev_out);

  //memcpy back
  cudaMemcpy(out,dev_out,sizeof(int)*N*N,cudaMemcpyDeviceToHost);

  //dev_output
  output(in);
  printf("
--------
");
  output(out);

  //cudaFree
  cudaFree(dev_in);
  cudaFree(dev_out);
  free(in);
  free(out);

  return 0;
}

统计数目

#include <iostream>
#include <stdlib.h>
#include <stdio.h>
#include "cuda.h"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"

const int N = 26;
const int L = 128;
const int S = L*4;
const int block = 4;
const int thread = 32;

__global__ void rec(char* book, int * record)
{
  unsigned int tid = threadIdx.x;

  __shared__ int temp[N];

  temp[tid] = 0;

  __syncthreads();

  int index = tid + blockIdx.x * blockDim.x;
  int offset = blockDim.x * gridDim.x;
  //printf("%d-%d
",index,offset);
  while(index < S)
  {
    atomicAdd(&(temp[book[index]]),1);
    index += offset;
  }
  __syncthreads();
  atomicAdd(&(record[tid]),temp[tid]);
}

int main()
{
   //host
   char * book;
   int * record;

   //device
   char * dev_book;
   int * dev_record;

   //host cudaMalloc
   book = (char*)malloc(sizeof(char)*S);
   record = (int*)malloc(sizeof(int)*N);

   //device malloc
   cudaMalloc((void**)&dev_book,sizeof(char)*S);
   cudaMalloc((void**)&dev_record,sizeof(int)*N);

   //init
   for(int i = 0 ; i < S ; i++)
   {
      srand(i+rand());
      book[i] = (i+i*i+rand())%26;
   }

   //cpu
   int tp[N]={0};
   for(int i = 0 ; i < S ; i++)
   {
     tp[book[i]]++;
   }
   for(int i = 0 ; i < N ; i++)
      printf("%d ",tp[i]);
   printf("
");

   //memcpy To device
   cudaMemcpy(dev_book,book,sizeof(char)*S,cudaMemcpyHostToDevice);

   //kernel functions
   rec<<<block,thread>>>(dev_book,dev_record);
   //memcpy To host
   cudaMemcpy(record,dev_record,sizeof(int)*N,cudaMemcpyDeviceToHost);
   //output
   for(int i = 0 ; i < N ; i++)
   {
     printf("%d ",record[i]);
   }
   printf("
");

   //destory
   cudaFree(dev_book);
   cudaFree(dev_record);
   free(book);
   free(record);

  return 0;
}

平方和求和

分块处理

#include <iostream>
#include <stdlib.h>
#include <stdio.h>
#include "cuda.h"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <time.h>

/*
*  author : pprp
*  theme : 平方和
*/
const int N = 128;
const int block = 4;
const int thread = 32;

__global__ void calc0(int * arr, int * result)
{
  int tid = threadIdx.x;
  int Size = N / block;
  int sum = 0;
  for(int i = tid * Size ; i <(tid+1)*Size; i++)
  {
    sum += arr[i]*arr[i];
  }
  result[tid] = sum;
  //printf("sum: %d
",sum);
}

int main()
{
  //host
  int * arr;
  int * result;

  //device
  int * dev_arr;
  int * dev_result;

  //host malloc
  arr = (int*)malloc(sizeof(int)*N);
  result = (int*)malloc(sizeof(int)*block);

  //device malloc
  cudaMalloc((void**)&dev_arr,sizeof(int)*N);
  cudaMalloc((void**)&dev_result,sizeof(int)*block);

  //init
  for(int i = 0 ; i < N ; i++)
  {
    arr[i] = i+1;
    if(i < block)
    {
      result[i] = 0;
    }
  }

  //cpu
  clock_t start,end;
  start = clock();
  unsigned int res = 0;
  for(int i = 0 ; i < N ; i++)
  {
    res += arr[i]*arr[i];
  }
  end = clock();
  printf("cpu ans : %d
cpu time: %.5f
",res,float(end-start));

  //time start
  cudaEvent_t st,ed;
  cudaEventCreate(&st);
  cudaEventCreate(&ed);
  cudaEventRecord(st,0);

  //memcpy To Host
  cudaMemcpy(dev_arr,arr,sizeof(int)*N,cudaMemcpyHostToDevice);

  //kernel functions
  calc0<<<1,4>>>(dev_arr,dev_result);
  //memcpy To Device
  cudaMemcpy(result,dev_result,sizeof(int)*block,cudaMemcpyDeviceToHost);

  //output
  int res2=0;
  for(int i = 0 ; i < block ; i++)
  {
    res2 += result[i];
    //printf("test: %d
",result[i]);
  }

  //time end
  cudaEventRecord(ed,0);
  cudaEventSynchronize(ed);
  float gpu_time;
  cudaEventElapsedTime(&gpu_time,st,ed);
  printf("gpu ans :%d
gpu time: %.5f
",res2,gpu_time);

  //time destroy
  cudaEventDestroy(st);
  cudaEventDestroy(ed);

  //device free
  cudaFree(dev_arr);
  cudaFree(dev_result);
  free(arr);
  free(result);

  return 0;
}

线程相邻

#include <iostream>
#include <stdlib.h>
#include <stdio.h>
#include "cuda.h"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <time.h>

/*
*  author : pprp
*  theme : 平方和
*/
const int N = 128;
const int block = 4;
const int thread = 32;

__global__ void calc0(int * arr, int * result)
{
  int tid = threadIdx.x;

  if(tid > block)return;

  int sum = 0;
  for(int i = tid; i < N ; i+=block)
  {
    sum += arr[i]*arr[i];
  }
  result[tid] = sum;
}

int main()
{
  //host
  int * arr;
  int * result;

  //device
  int * dev_arr;
  int * dev_result;

  //host malloc
  arr = (int*)malloc(sizeof(int)*N);
  result = (int*)malloc(sizeof(int)*block);

  //device malloc
  cudaMalloc((void**)&dev_arr,sizeof(int)*N);
  cudaMalloc((void**)&dev_result,sizeof(int)*block);

  //init
  for(int i = 0 ; i < N ; i++)
  {
    arr[i] = i+1;
    if(i < block)
    {
      result[i] = 0;
    }
  }

  //cpu
  clock_t start,end;
  start = clock();
  unsigned int res = 0;
  for(int i = 0 ; i < N ; i++)
  {
    res += arr[i]*arr[i];
  }
  end = clock();
  printf("cpu ans : %d
cpu time: %.5f
",res,float(end-start));

  //time start
  cudaEvent_t st,ed;
  cudaEventCreate(&st);
  cudaEventCreate(&ed);
  cudaEventRecord(st,0);

  //memcpy To Host
  cudaMemcpy(dev_arr,arr,sizeof(int)*N,cudaMemcpyHostToDevice);

  //kernel functions
  calc0<<<1,block>>>(dev_arr,dev_result);
  //memcpy To Device
  cudaMemcpy(result,dev_result,sizeof(int)*block,cudaMemcpyDeviceToHost);

  //output
  int res2=0;
  for(int i = 0 ; i < block ; i++)
  {
    res2 += result[i];
    //printf("test: %d
",result[i]);
  }

  //time end
  cudaEventRecord(ed,0);
  cudaEventSynchronize(ed);
  float gpu_time;
  cudaEventElapsedTime(&gpu_time,st,ed);
  printf("gpu ans :%d
gpu time: %.5f
",res2,gpu_time);

  //time destroy
  cudaEventDestroy(st);
  cudaEventDestroy(ed);

  //device free
  cudaFree(dev_arr);
  cudaFree(dev_result);
  free(arr);
  free(result);

  return 0;
}

多block计算

#include <iostream>
#include <stdlib.h>
#include <stdio.h>
#include "cuda.h"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <time.h>

/*
*  author : pprp
*  theme : 平方和
*/
const int N = 32;
const int block = 4;
const int thread = 8;

__global__ void calc0(int * arr, int * result)
{
  int tid = threadIdx.x;
  int bid = blockIdx.x;
 

  int sum = 0;
  for(int i = bid*blockDim.x+tid; i < N ; i += blockDim.x*gridDim.x)
  {
    sum += arr[i]*arr[i];
  }
  __syncthreads();
  result[bid*blockDim.x+tid] = sum;
  printf("++%d 
",sum);
}

int main()
{
  //host
  int * arr;
  int * result;

  //device
  int * dev_arr;
  int * dev_result;

  //host malloc
  arr = (int*)malloc(sizeof(int)*N);
  result = (int*)malloc(sizeof(int)*N);

  //device malloc
  cudaMalloc((void**)&dev_arr,sizeof(int)*N);
  cudaMalloc((void**)&dev_result,sizeof(int)*N);

  //init
  for(int i = 0 ; i < N ; i++)
  {
    arr[i] = i+1;
    if(i < thread)
    {
      result[i] = 0;
    }
  }

  //cpu
  clock_t start,end;
  start = clock();
  unsigned int res = 0;
  for(int i = 0 ; i < N ; i++)
  {
    res += arr[i]*arr[i];
  }
  end = clock();
  printf("cpu ans : %d
cpu time: %.5f
",res,float(end-start));

  //time start
  cudaEvent_t st,ed;
  cudaEventCreate(&st);
  cudaEventCreate(&ed);
  cudaEventRecord(st,0);

  //memcpy To Host
  cudaMemcpy(dev_arr,arr,sizeof(int)*N,cudaMemcpyHostToDevice);

  //kernel functions
  calc0<<<block,thread>>>(dev_arr,dev_result);
  //memcpy To Device
  cudaMemcpy(result,dev_result,sizeof(int)*N,cudaMemcpyDeviceToHost);

  //output
  int res2=0;
  for(int i = 0 ; i < N ; i++)
  {
    res2 += result[i];
    //printf("test: %d
",result[i]);
  }

  //time end
  cudaEventRecord(ed,0);
  cudaEventSynchronize(ed);
  float gpu_time;
  cudaEventElapsedTime(&gpu_time,st,ed);
  printf("gpu ans :%d
gpu time: %.5f
",res2,gpu_time);

  //time destroy
  cudaEventDestroy(st);
  cudaEventDestroy(ed);

  //device free
  cudaFree(dev_arr);
  cudaFree(dev_result);
  free(arr);
  free(result);

  return 0;
}
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原文地址：https://www.cnblogs.com/pprp/p/9960146.html