cuda编程，很简单的累加运算，为啥核函数的调用会显示不允许
//************************** *********************
// 2024-3-17-
// ************************** *********************
#include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include <string.h>
//#include <device_functions.h>
//使用全局内存来操作
__global__ void global_reduce_kernel(float* d_out, float* d_in)
{
int myId = threadIdx.x + blockIdx.x * blockDim.x;//有多个block.在所有线程的位置
int tid = threadIdx.x;//在当前block里面的位置
//在global memory里面做
//循环的条件是 s 大于 0，每次循环迭代结束后，s 的值右移一位（相当于将其除以 2），以便在下一次迭代中处理更小的数据块
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1)
{
if (tid < s)
{
d_in += d_in;
}
__syncthreads();//每个线程块每次循环结束之后才进行下一次运行
}
//最后要的是线程块的所有计算的求和结果
if (tid == 0)
{
d_out = d_in;
}
}
//使用共享内存实现归约
__global__ void share_reduce_kernel(float* d_out, float* d_in)
{
extern __shared__ float sdata[];//给每一个block定义他们各自的内存；
int myId = threadIdx.x + blockIdx.x * blockDim.x;//有多个block.在所有线程的位置
int tid = threadIdx.x;//在当前block里面的位置
//然后将全局内存的数据复制给共享内存, 之后就不需要再从全局内存读数据，
//共享数据读取的速度快于全局内存
sdata = d_in;
__syncthreads();
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1)
{
if (tid < s)
{
d_in += d_in;
}
__syncthreads();//每个线程块每次循环结束之后才进行下一次运行
}
//最后要的是线程块的所有计算的求和结果
if (tid == 0)
{
d_out = d_in;
}
}
void reduce(float* d_out, float* d_intermediate, float* d_in, int size, bool usesShareMemory)
{
const int maxThreadsPerBlock = 1024;
int threads = maxThreadsPerBlock;
int blocks = size / maxThreadsPerBlock;
//这个地方对每个
if (usesShareMemory)
{
share_reduce_kernel <<< blocks, threads, threads* sizeof(float) >>> (float* d_intermediate, float* d_in);
}
else
{
global_reduce_kernel << < blocks, threads >> > (float* d_intermediate, float* d_in);
}
threads = blocks;
blocks = 1;
if (usesShareMemory)
{
if (usesShareMemory)
{
share_reduce_kernel << < blocks, threads, threads * sizeof(float) >> > (float* d_intermediate, float* d_in);
}
else
{
global_reduce_kernel << < blocks, threads >> > (float* d_intermediate, float* d_in);
}
}
}
int main(int argc, char** argv)
{
int deviceCount;
cudaGetDeviceCount(&deviceCount);
if (deviceCount == 0)
{
fprintf(stderr, "error: no device surporting\n");
exit(EXIT_FAILURE);
}
int dev = 0;
cudaSetDevice(dev);
cudaDeviceProp devProps;
if (cudaGetDeviceProperties(&devProps, dev) == 0)
{
printf("using device %d:\n,", dev);
printf("%s: global memory: %dB; compute v%d.%d; clock: %d kHZ\n",
devProps.name, (int)devProps.totalGlobalMem, (int)devProps.major, (int)devProps.major, (int)devProps.clockRate);
}
const int ARRAY_SIZE = 1 << 20;
const int ARRAY_BITE = ARRAY_SIZE * sizeof(float);
//在cpu上创建存储输入数据的数组
float h_in;
float sum = 0.0f;
for (int i = 0; i < ARRAY_SIZE; i++)
{
h_in = -1.0f + (float)random() / ((float)RAND_MAX / 2.0f);
sum += h_in;
}
//declare GPU memory pointers
float* d_in, *d_out, * d_intermediate;
//在GPU上创建存储待处理数据和处理结果的数组
cudaMalloc((void**)&d_in, ARRAY_BITE);
cudaMalloc((void**)&d_intermediate, ARRAY_BITE);
cudaMalloc((void**)&d_out, sizeof(float));
//将CPU输入数据输入到GPU
cudaMemcpy(d_in, h_in, ARRAY_BITE, cudaMemcpyHostToDevice);
int whichKernel = 0;
if (argc == 2)
{
whichKernel = atoi(argv);
}
}
在你的代码中，出现了几个语法错误导致编译错误。我将逐个指出并给出修正后的代码。
首先，在`global_reduce_kernel`函数中，你想对全局内存的变量进行累加操作，但是你写成了`d_in += d_in`，这是错误的。应该使用`*d_in += *d_in`来对变量进行累加。此外，在最后的求和结果处，你写成了`d_out = d_in`，实际上应该是`*d_out = *d_in`。修正后的代码如下：
__global__ void global_reduce_kernel(float* d_out, float* d_in)
{
int myId = threadIdx.x + blockIdx.x * blockDim.x;
int tid = threadIdx.x;
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1)
{
if (tid < s)
{
*d_in += *d_in;
}
__syncthreads();
}
if (tid == 0)
{
*d_out = *d_in;
}
}
接下来，在`share_reduce_kernel`函数中，你定义了一个共享内存`sdata`，但是你忘记给它分配实际的内存空间。共享内存的大小应该是每个线程块的大小（即`blockDim.x * sizeof(float)`）。修正后的代码如下：
__global__ void share_reduce_kernel(float* d_out, float* d_in)
{
extern __shared__ float sdata[];
int myId = threadIdx.x + blockIdx.x * blockDim.x;
int tid = threadIdx.x;
sdata = d_in;
__syncthreads();
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1)
{
if (tid < s)
{
sdata += sdata;
}
__syncthreads();
}
if (tid == 0)
{
*d_out = sdata;
}
}
在`reduce`函数中，你调用核函数的方式不正确。应该使用`<<<...>>>`语法来调用核函数，并且传递的参数应该是设备端的指针。修正后的代码如下：
void reduce(float* d_out, float* d_intermediate, float* d_in, int size, bool usesShareMemory)
{
const int maxThreadsPerBlock = 1024;
int threads = maxThreadsPerBlock;
int blocks = size / maxThreadsPerBlock;
if (usesShareMemory)
{
share_reduce_kernel<<<blocks, threads, threads * sizeof(float)>>>(d_intermediate, d_in);
}
else
{
global_reduce_kernel<<<blocks, threads>>>(d_intermediate, d_in);
}
threads = blocks;
blocks = 1;
if (usesShareMemory)
{
if (usesShareMemory)
{
share_reduce_kernel<<<blocks, threads, threads * sizeof(float)>>>(d_out, d_intermediate);
}
else
{
global_reduce_kernel<<<blocks, threads>>>(d_out, d_intermediate);
}
}
}
最后，在`main`函数中，你定义了一个常量`ARRAY_SIZE`但没有给它赋值。请为它赋予一个合适的值，比如`1000`。修正后的代码如下：
int main(int argc, char** argv)
{
// ...
const int ARRAY_SIZE = 1000;
// ...
}
希望这些修正能够解决你遇到的问题。如果还有其他问题，请随时提问！
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