非常感谢您阅读我的主题。
我正在做CUDA工作，但是继续得到cudaDeviceSynchronize（）错误代码77：cudaErrorIllegalAddress，不知道为什么。我搜索了代码和函数，令人惊讶的是，只有少数记录显示出来。很奇怪。
我基本上总结了图像的所有像素。为了让我的问题尽可能多地参考，我在这里展示了我所有的CUDA代码：
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "thorcalgpu.h"
#include <stdio.h>
#include "math.h"
#include <vector>
#include <algorithm>
#include <stdlib.h>
#include <stdio.h>
#include <vector>
#include <numeric>
#include <iostream>
using namespace std;
float random_float(void)
{
return static_cast<float>(rand()) / RAND_MAX;
}
__global__ void reduceSum(unsigned short *input,
unsigned long long *per_block_results,
const int n)
{
extern __shared__ unsigned long long sdata[];
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
// load input into __shared__ memory
unsigned short x = 0;
if(i < n)
{
x = input[i];
}
sdata[threadIdx.x] = x;
__syncthreads();
// contiguous range pattern
for(int offset = blockDim.x / 2; offset > 0; offset >>= 1)
{
if(threadIdx.x < offset)
{
// add a partial sum upstream to our own
sdata[threadIdx.x] += sdata[threadIdx.x + offset];
}
// wait until all threads in the block have
// updated their partial sums
__syncthreads();
}
// thread 0 writes the final result
if(threadIdx.x == 0)
{
per_block_results[blockIdx.x] = sdata[0];
}
}
// Helper function for using CUDA to add vectors in parallel.
//template <class T>
cudaError_t gpuWrapper(float *mean, int N, vector<string> filelist)
{
int size = N*N;
unsigned long long* dev_sum = 0;
unsigned short* dev_img = 0;
cudaError_t cudaStatus;
const int block_size = 512;
const int num_blocks = (size/block_size) + ((size%block_size) ? 1 : 0);
int L = filelist.size();
// Choose which GPU to run on, change this on a multi-GPU system.
double totalgpuinittime = 0;
StartCounter(7);
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 vectors (two input, one output) .
cudaStatus = cudaMalloc((void**)&dev_img, size * sizeof(unsigned short));
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
cudaStatus = cudaMalloc((void**)&dev_sum, num_blocks*sizeof(unsigned long long));
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
totalgpuinittime = GetCounter(7);
unsigned short* img;
unsigned short* pimg;
unsigned long long* sum = new unsigned long long[num_blocks];
unsigned long long* psum = sum;
cout<<endl;
cout << "gpu looping starts, and in progress ..." << endl;
StartCounter(6);
double totalfileiotime = 0;
double totalh2dcpytime = 0;
double totalkerneltime = 0;
double totald2hcpytime = 0;
double totalcpusumtime = 0;
double totalloopingtime = 0;
for (int k = 0; k < L; k++)
{
StartCounter(1);
img = (unsigned short*)LoadTIFF(filelist[k].c_str());
totalfileiotime += GetCounter(1);
psum = sum;
pimg = img;
float gpumean = 0;
memset(psum, 0, sizeof(unsigned long long)*num_blocks);
StartCounter(2);
// Copy input vectors from host memory to GPU buffers.
cudaStatus = cudaMemcpy(dev_img, pimg, size * sizeof(unsigned short), cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
cudaStatus = cudaMemcpy(dev_sum, psum, num_blocks*sizeof(unsigned long long), cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
totalh2dcpytime += GetCounter(2);
StartCounter(3);
//reduceSum<<<num_blocks,block_size,num_blocks * sizeof(unsigned long long)>>>(dev_img, dev_sum, size);
//reduceSum<<<num_blocks,block_size,block_size * sizeof(unsigned short)>>>(dev_img, dev_sum, size);
reduceSum<<<num_blocks,block_size>>>(dev_img, dev_sum, size);
totalkerneltime += GetCounter(3);
// Check for any errors launching the kernel
cudaStatus = cudaGetLastError();
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "reduction Kernel launch failed: %s\n", cudaGetErrorString(cudaStatus));
goto Error;
}
// cudaDeviceSynchronize waits for the kernel to finish, and returns
// any errors encountered during the launch.
// !!!!!! following is where the code 77 error occurs!!!!!!!
cudaStatus = cudaDeviceSynchronize();
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching addKernel!\n", cudaStatus);
goto Error;
}
// Copy output vector from GPU buffer to host memory.
StartCounter(4);
cudaStatus = cudaMemcpy(psum, dev_sum, num_blocks * sizeof(unsigned long long ), cudaMemcpyDeviceToHost);
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
totald2hcpytime += GetCounter(4);
StartCounter(5);
for (int i = 0; i < num_blocks; i++)
{
gpumean += *psum;
psum++;
}
gpumean /= N*N;
totalcpusumtime += GetCounter(5);
delete img;
img = NULL;
cout<<gpumean<<endl;
}
int S = 1e+6;
int F = filelist.size();
float R = S/F;
totalloopingtime = GetCounter(6);
cout<<"gpu looping ends."<<endl<<endl;
cout<< "analysis:"<<endl;
cout<<"gpu initialization time: "<<totalgpuinittime<<" sec"<<endl<<endl;
cout<<"file I/O time: "<<endl;
cout<<" total "<<totalfileiotime<<" sec | average "<<totalfileiotime*R<<" usec/frame"<<endl<<endl;
cout<<"host-to-device copy time: "<<endl;
cout<<" total "<<totalh2dcpytime<<" sec | average "<<totalh2dcpytime*R<<" usec/frame"<<endl<<endl;
cout<<"pure gpu kerneling time: "<<endl;
cout<<" total "<<totalkerneltime<<" sec | average "<<totalkerneltime*R<<" usec/frame"<<endl<<endl;
cout<<"device-to-host copy time: "<<endl;
cout<<" total "<<totald2hcpytime<<" sec | average "<<totald2hcpytime*R<<" usec/frame"<<endl<<endl;
/*cout<<"cpu summing time: "<<endl;
cout<<" total: "<<totalcpusumtime<<" sec | average: "<<totalcpusumtime*R<<" usec/frame"<<endl<<endl;;*/
/*cout <<"gpu looping time: " << endl;
cout<<" total: "<<totalloopingtime<<" sec | average: "<<totalloopingtime*R<<" usec/frame"<<endl;*/
Error:
cudaFree(dev_sum);
cudaFree(dev_img);
delete sum;
sum = NULL;
return cudaStatus;
}
void kernel(float* &mean, int N, vector<string> filelist)
{
// wrapper and kernel
cudaError_t cudaStatus = gpuWrapper(mean, N, filelist);
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "gpuWapper failed!");
}
// printf("mean is: %f\n", mean);
// cudaDeviceReset must be called before exiting in order for profiling and
// tracing tools such as Nsight and Visual Profiler to show complete traces.
StartCounter(8);
cudaStatus = cudaDeviceReset();
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaDeviceReset failed!");
}
cout<<"gpu reset time: "<<GetCounter(8)<<" sec"<<endl<<endl;
//return *mean;
}
我为主机和设备内存分配了足够的等效内存空间。任何意见表示赞赏。
答案 0 :(得分：8)
虽然这可能不是代码中唯一的错误来源，但您没有为还原内核分配任何动态共享内存，从而导致您看到的非法寻址错误。正确的内核启动应该类似于
size_t shm_size = block_size * sizeof(unsigned long long);
reduceSum<<<num_blocks,block_size,shm_size>>>(dev_img, dev_sum, size);
这为在还原内核中运行的每个线程分配一个无符号long long的等价物，这（通过我粗略阅读你的代码）应该使共享内存数组sdata的内核大小正确在没有对该数组的越界访问的情况下运行。