我正在编写一个函数，它将找到最小值和使用CUDA找到值为1D数组的索引。
我开始修改缩减代码，以便在1d数组中查找值的总和。该代码适用于sum函数，但我无法让它找到最小值。如果有任何cuda guru，请在邮件中附上代码，请指出我正在做的错误。
实际功能如下，在测试示例中，数组大小为1024.所以，我正在使用shuffle reduction部分。问题是每个块的g_oIdxs（给出索引）的输出值，并且与普通顺序CPU代码相比，每个块的g_odata（给出最小值）是错误的。
当我在主机中打印时，g_odata中的值全为零（0）。
提前致谢！
#include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
#include <math.h>
#include <unistd.h>
#include <sys/time.h>
#if __DEVICE_EMULATION__
#define DEBUG_SYNC __syncthreads();
#else
#define DEBUG_SYNC
#endif
#ifndef MIN
#define MIN(x,y) ((x < y) ? x : y)
#endif
#ifndef MIN_IDX
#define MIN_IDX(x,y, idx_x, idx_y) ((x < y) ? idx_x : idx_y)
#endif
#if (__CUDA_ARCH__ < 200)
#define int_mult(x,y) __mul24(x,y)
#else
#define int_mult(x,y) x*y
#endif
#define inf 0x7f800000
bool isPow2(unsigned int x)
{
return ((x&(x-1))==0);
}
unsigned int nextPow2(unsigned int x)
{
--x;
x |= x >> 1;
x |= x >> 2;
x |= x >> 4;
x |= x >> 8;
x |= x >> 16;
return ++x;
}
// Utility class used to avoid linker errors with extern
// unsized shared memory arrays with templated type
template<class T>
struct SharedMemory {
__device__ inline operator T *() {
extern __shared__ int __smem[];
return (T *) __smem;
}
__device__ inline operator const T *() const {
extern __shared__ int __smem[];
return (T *) __smem;
}
};
// specialize for double to avoid unaligned memory
// access compile errors
template<>
struct SharedMemory<double> {
__device__ inline operator double *() {
extern __shared__ double __smem_d[];
return (double *) __smem_d;
}
__device__ inline operator const double *() const {
extern __shared__ double __smem_d[];
return (double *) __smem_d;
}
};
/*
This version adds multiple elements per thread sequentially. This reduces the overall
cost of the algorithm while keeping the work complexity O(n) and the step complexity O(log n).
(Brent's Theorem optimization)
Note, this kernel needs a minimum of 64*sizeof(T) bytes of shared memory.
In other words if blockSize <= 32, allocate 64*sizeof(T) bytes.
If blockSize > 32, allocate blockSize*sizeof(T) bytes.
*/
template<class T, unsigned int blockSize, bool nIsPow2>
__global__ void reduce6(T *g_idata, T *g_odata, unsigned int n) {
T *sdata = SharedMemory<T>();
// perform first level of reduction,
// reading from global memory, writing to shared memory
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockSize * 2 + threadIdx.x;
unsigned int gridSize = blockSize * 2 * gridDim.x;
T mySum = 0;
// we reduce multiple elements per thread. The number is determined by the
// number of active thread blocks (via gridDim). More blocks will result
// in a larger gridSize and therefore fewer elements per thread
while (i < n) {
mySum += g_idata[i];
// ensure we don't read out of bounds -- this is optimized away for powerOf2 sized arrays
if (nIsPow2 || i + blockSize < n)
mySum += g_idata[i + blockSize];
i += gridSize;
}
// each thread puts its local sum into shared memory
sdata[tid] = mySum;
__syncthreads();
// do reduction in shared mem
if ((blockSize >= 512) && (tid < 256)) {
sdata[tid] = mySum = mySum + sdata[tid + 256];
}
__syncthreads();
if ((blockSize >= 256) && (tid < 128)) {
sdata[tid] = mySum = mySum + sdata[tid + 128];
}
__syncthreads();
if ((blockSize >= 128) && (tid < 64)) {
sdata[tid] = mySum = mySum + sdata[tid + 64];
}
__syncthreads();
#if (__CUDA_ARCH__ >= 300 )
if (tid < 32) {
// Fetch final intermediate sum from 2nd warp
if (blockSize >= 64)
mySum += sdata[tid + 32];
// Reduce final warp using shuffle
for (int offset = warpSize / 2; offset > 0; offset /= 2) {
mySum += __shfl_down(mySum, offset);
}
}
#else
// fully unroll reduction within a single warp
if ((blockSize >= 64) && (tid < 32))
{
sdata[tid] = mySum = mySum + sdata[tid + 32];
}
__syncthreads();
if ((blockSize >= 32) && (tid < 16))
{
sdata[tid] = mySum = mySum + sdata[tid + 16];
}
__syncthreads();
if ((blockSize >= 16) && (tid < 8))
{
sdata[tid] = mySum = mySum + sdata[tid + 8];
}
__syncthreads();
if ((blockSize >= 8) && (tid < 4))
{
sdata[tid] = mySum = mySum + sdata[tid + 4];
}
__syncthreads();
if ((blockSize >= 4) && (tid < 2))
{
sdata[tid] = mySum = mySum + sdata[tid + 2];
}
__syncthreads();
if ((blockSize >= 2) && ( tid < 1))
{
sdata[tid] = mySum = mySum + sdata[tid + 1];
}
__syncthreads();
#endif
// write result for this block to global mem
if (tid == 0)
g_odata[blockIdx.x] = mySum;
}
/*
This version adds multiple elements per thread sequentially. This reduces the overall
cost of the algorithm while keeping the work complexity O(n) and the step complexity O(log n).
(Brent's Theorem optimization)
Note, this kernel needs a minimum of 64*sizeof(T) bytes of shared memory.
In other words if blockSize <= 32, allocate 64*sizeof(T) bytes.
If blockSize > 32, allocate blockSize*sizeof(T) bytes.
*/
template<class T, unsigned int blockSize, bool nIsPow2>
__global__ void reduceMin6(T *g_idata, int *g_idxs, T *g_odata, int *g_oIdxs, unsigned int n) {
T *sdata = SharedMemory<T>();
int *sdataIdx = SharedMemory<int>();
// perform first level of reduction,
// reading from global memory, writing to shared memory
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockSize * 2 + threadIdx.x;
unsigned int gridSize = blockSize * 2 * gridDim.x;
T myMin = 99999;
int myMinIdx = -1;
// we reduce multiple elements per thread. The number is determined by the
// number of active thread blocks (via gridDim). More blocks will result
// in a larger gridSize and therefore fewer elements per thread
while (i < n) {
myMinIdx = MIN_IDX(g_idata[i], myMin, g_idxs[i], myMinIdx);
myMin = MIN(g_idata[i], myMin);
// ensure we don't read out of bounds -- this is optimized away for powerOf2 sized arrays
if (nIsPow2 || i + blockSize < n){
//myMin += g_idata[i + blockSize];
myMinIdx = MIN_IDX(g_idata[i + blockSize], myMin, g_idxs[i + blockSize], myMinIdx);
myMin = MIN(g_idata[i + blockSize], myMin);
}
i += gridSize;
}
// each thread puts its local sum into shared memory
sdata[tid] = myMin;
sdataIdx[tid] = myMinIdx;
__syncthreads();
// do reduction in shared mem
if ((blockSize >= 512) && (tid < 256)) {
//sdata[tid] = mySum = mySum + sdata[tid + 256];
sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 256], myMin, sdataIdx[tid + 256], myMinIdx);
sdata[tid] = myMin = MIN(sdata[tid + 256], myMin);
}
__syncthreads();
if ((blockSize >= 256) && (tid < 128)) {
//sdata[tid] = myMin = myMin + sdata[tid + 128];
sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 128], myMin, sdataIdx[tid + 128], myMinIdx);
sdata[tid] = myMin = MIN(sdata[tid + 128], myMin);
}
__syncthreads();
if ((blockSize >= 128) && (tid < 64)) {
//sdata[tid] = myMin = myMin + sdata[tid + 64];
sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 64], myMin, sdataIdx[tid + 64], myMinIdx);
sdata[tid] = myMin = MIN(sdata[tid + 64], myMin);
}
__syncthreads();
#if (__CUDA_ARCH__ >= 300 )
if (tid < 32) {
// Fetch final intermediate sum from 2nd warp
if (blockSize >= 64){
//myMin += sdata[tid + 32];
myMinIdx = MIN_IDX(sdata[tid + 32], myMin, sdataIdx[tid + 32], myMinIdx);
myMin = MIN(sdata[tid + 32], myMin);
}
// Reduce final warp using shuffle
for (int offset = warpSize / 2; offset > 0; offset /= 2) {
//myMin += __shfl_down(myMin, offset);
int tempMyMinIdx = __shfl_down(myMinIdx, offset);
float tempMyMin = __shfl_down(myMin, offset);
myMinIdx = MIN_IDX(tempMyMin, myMin, tempMyMinIdx , myMinIdx);
myMin = MIN(tempMyMin, myMin);
}
}
#else
// fully unroll reduction within a single warp
if ((blockSize >= 64) && (tid < 32))
{
//sdata[tid] = myMin = myMin + sdata[tid + 32];
sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 32], myMin, sdataIdx[tid + 32], myMinIdx);
sdata[tid] = myMin = MIN(sdata[tid + 32], myMin);
}
__syncthreads();
if ((blockSize >= 32) && (tid < 16))
{
//sdata[tid] = myMin = myMin + sdata[tid + 16];
sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 16], myMin, sdataIdx[tid + 16], myMinIdx);
sdata[tid] = myMin = MIN(sdata[tid + 16], myMin);
}
__syncthreads();
if ((blockSize >= 16) && (tid < 8))
{
//sdata[tid] = myMin = myMin + sdata[tid + 8];
sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 8], myMin, sdataIdx[tid + 8], myMinIdx);
sdata[tid] = myMin = MIN(sdata[tid + 8], myMin);
}
__syncthreads();
if ((blockSize >= 8) && (tid < 4))
{
//sdata[tid] = myMin = myMin + sdata[tid + 4];
sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 4], myMin, sdataIdx[tid + 4], myMinIdx);
sdata[tid] = myMin = MIN(sdata[tid + 4], myMin);
}
__syncthreads();
if ((blockSize >= 4) && (tid < 2))
{
//sdata[tid] = myMin = myMin + sdata[tid + 2];
sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 2], myMin, sdataIdx[tid + 2], myMinIdx);
sdata[tid] = myMin = MIN(sdata[tid + 2], myMin);
}
__syncthreads();
if ((blockSize >= 2) && ( tid < 1))
{
//sdata[tid] = myMin = myMin + sdata[tid + 1];
sdataIdx[tid] = myMinIdx = MIN_IDX(sdata[tid + 1], myMin, sdataIdx[tid + 1], myMinIdx);
sdata[tid] = myMin = MIN(sdata[tid + 1], myMin);
}
__syncthreads();
#endif
__syncthreads();
// write result for this block to global mem
if (tid == 0){
g_odata[blockIdx.x] = myMin;
g_oIdxs[blockIdx.x] = myMinIdx;
}
}
////////////////////////////////////////////////////////////////////////////////
// Compute the number of threads and blocks to use for the given reduction kernel
// For the kernels >= 3, we set threads / block to the minimum of maxThreads and
// n/2. For kernels < 3, we set to the minimum of maxThreads and n. For kernel
// 6, we observe the maximum specified number of blocks, because each thread in
// that kernel can process a variable number of elements.
////////////////////////////////////////////////////////////////////////////////
void getNumBlocksAndThreads(int whichKernel, int n, int maxBlocks,
int maxThreads, int &blocks, int &threads) {
//get device capability, to avoid block/grid size exceed the upper bound
cudaDeviceProp prop;
int device;
cudaGetDevice(&device);
cudaGetDeviceProperties(&prop, device);
if (whichKernel < 3) {
threads = (n < maxThreads) ? nextPow2(n) : maxThreads;
blocks = (n + threads - 1) / threads;
} else {
threads = (n < maxThreads * 2) ? nextPow2((n + 1) / 2) : maxThreads;
blocks = (n + (threads * 2 - 1)) / (threads * 2);
}
if ((float) threads * blocks
> (float) prop.maxGridSize[0] * prop.maxThreadsPerBlock) {
printf("n is too large, please choose a smaller number!\n");
}
if (blocks > prop.maxGridSize[0]) {
printf(
"Grid size <%d> exceeds the device capability <%d>, set block size as %d (original %d)\n",
blocks, prop.maxGridSize[0], threads * 2, threads);
blocks /= 2;
threads *= 2;
}
if (whichKernel == 6) {
blocks = MIN(maxBlocks, blocks);
}
}
////////////////////////////////////////////////////////////////////////////////
// Wrapper function for kernel launch
////////////////////////////////////////////////////////////////////////////////
template<class T>
void reduce(int size, int threads, int blocks, int whichKernel, T *d_idata,
T *d_odata) {
dim3 dimBlock(threads, 1, 1);
dim3 dimGrid(blocks, 1, 1);
// when there is only one warp per block, we need to allocate two warps
// worth of shared memory so that we don't index shared memory out of bounds
int smemSize =
(threads <= 32) ? 2 * threads * sizeof(T) : threads * sizeof(T);
if (isPow2(size)) {
switch (threads) {
case 512:
reduce6<T, 512, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 256:
reduce6<T, 256, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 128:
reduce6<T, 128, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 64:
reduce6<T, 64, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 32:
reduce6<T, 32, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 16:
reduce6<T, 16, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 8:
reduce6<T, 8, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 4:
reduce6<T, 4, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 2:
reduce6<T, 2, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 1:
reduce6<T, 1, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
}
} else {
switch (threads) {
case 512:
reduce6<T, 512, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 256:
reduce6<T, 256, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 128:
reduce6<T, 128, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 64:
reduce6<T, 64, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 32:
reduce6<T, 32, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 16:
reduce6<T, 16, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 8:
reduce6<T, 8, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 4:
reduce6<T, 4, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 2:
reduce6<T, 2, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
case 1:
reduce6<T, 1, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata,
d_odata, size);
break;
}
}
}
////////////////////////////////////////////////////////////////////////////////
// Wrapper function for kernel launch
////////////////////////////////////////////////////////////////////////////////
template<class T>
void reduceMin(int size, int threads, int blocks, int whichKernel, T *d_idata,
T *d_odata, int *idxs, int *oIdxs) {
dim3 dimBlock(threads, 1, 1);
dim3 dimGrid(blocks, 1, 1);
// when there is only one warp per block, we need to allocate two warps
// worth of shared memory so that we don't index shared memory out of bounds
int smemSize =
(threads <= 32) ? 2 * threads * sizeof(T) : threads * sizeof(T);
if (isPow2(size)) {
switch (threads) {
case 512:
reduceMin6<T, 512, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 256:
reduceMin6<T, 256, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 128:
reduceMin6<T, 128, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 64:
reduceMin6<T, 64, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 32:
reduceMin6<T, 32, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 16:
reduceMin6<T, 16, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 8:
reduceMin6<T, 8, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 4:
reduceMin6<T, 4, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 2:
reduceMin6<T, 2, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 1:
reduceMin6<T, 1, true> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
}
} else {
switch (threads) {
case 512:
reduceMin6<T, 512, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 256:
reduceMin6<T, 256, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 128:
reduceMin6<T, 128, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 64:
reduceMin6<T, 64, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 32:
reduceMin6<T, 32, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 16:
reduceMin6<T, 16, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 8:
reduceMin6<T, 8, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 4:
reduceMin6<T, 4, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 2:
reduceMin6<T, 2, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
case 1:
reduceMin6<T, 1, false> <<<dimGrid, dimBlock, smemSize>>>(d_idata, idxs,
d_odata, oIdxs, size);
break;
}
}
}
////////////////////////////////////////////////////////////////////////////////
//! Compute sum reduction on CPU
//! We use Kahan summation for an accurate sum of large arrays.
//! http://en.wikipedia.org/wiki/Kahan_summation_algorithm
//!
//! @param data pointer to input data
//! @param size number of input data elements
////////////////////////////////////////////////////////////////////////////////
template<class T>
void reduceMINCPU(T *data, int size, T *min, int *idx)
{
*min = data[0];
int min_idx = 0;
T c = (T)0.0;
for (int i = 1; i < size; i++)
{
T y = data[i];
T t = MIN(*min, y);
min_idx = MIN_IDX(*min, y, min_idx, i);
(*min) = t;
}
*idx = min_idx;
return;
}
////////////////////////////////////////////////////////////////////////////////
//! Compute sum reduction on CPU
//! We use Kahan summation for an accurate sum of large arrays.
//! http://en.wikipedia.org/wiki/Kahan_summation_algorithm
//!
//! @param data pointer to input data
//! @param size number of input data elements
////////////////////////////////////////////////////////////////////////////////
template<class T>
T reduceCPU(T *data, int size)
{
T sum = data[0];
T c = (T)0.0;
for (int i = 1; i < size; i++)
{
T y = data[i] - c;
T t = sum + y;
c = (t - sum) - y;
sum = t;
}
return sum;
}
// Instantiate the reduction function for 3 types
template void
reduce<int>(int size, int threads, int blocks, int whichKernel, int *d_idata,
int *d_odata);
template void
reduce<float>(int size, int threads, int blocks, int whichKernel,
float *d_idata, float *d_odata);
template void
reduce<double>(int size, int threads, int blocks, int whichKernel,
double *d_idata, double *d_odata);
// Instantiate the reduction function for 3 types
template void
reduceMin<int>(int size, int threads, int blocks, int whichKernel, int *d_idata,
int *d_odata, int *idxs, int *oIdxs);
template void
reduceMin<float>(int size, int threads, int blocks, int whichKernel, float *d_idata,
float *d_odata, int *idxs, int *oIdxs);
template void
reduceMin<double>(int size, int threads, int blocks, int whichKernel, double *d_idata,
double *d_odata, int *idxs, int *oIdxs);
unsigned long long int my_min_max_test(int num_els) {
// timers
unsigned long long int start;
unsigned long long int delta;
int maxThreads = 256; // number of threads per block
int whichKernel = 6;
int maxBlocks = 64;
int testIterations = 100;
float* d_in = NULL;
float* d_out = NULL;
int *d_idxs = NULL;
int *d_oIdxs = NULL;
printf("%d elements\n", num_els);
printf("%d threads (max)\n", maxThreads);
int numBlocks = 0;
int numThreads = 0;
getNumBlocksAndThreads(whichKernel, num_els, maxBlocks, maxThreads, numBlocks,
numThreads);
// in[1024] = 34.0f;
// in[333] = 55.0f;
// in[23523] = -42.0f;
// cudaMalloc((void**) &d_in, size);
// cudaMalloc((void**) &d_out, size);
// cudaMalloc((void**) &d_idxs, num_els * sizeof(int));
cudaMalloc((void **) &d_in, num_els * sizeof(float));
cudaMalloc((void **) &d_idxs, num_els * sizeof(int));
cudaMalloc((void **) &d_out, numBlocks * sizeof(float));
cudaMalloc((void **) &d_oIdxs, numBlocks * sizeof(int));
float* in = (float*) malloc(num_els * sizeof(float));
int *idxs = (int*) malloc(num_els * sizeof(int));
float* out = (float*) malloc(numBlocks * sizeof(float));
int* oIdxs = (int*) malloc(numBlocks * sizeof(int));
for (int i = 0; i < num_els; i++) {
in[i] = (double) rand() / (double) RAND_MAX;
idxs[i] = i;
}
for (int i = 0; i < num_els; i++) {
printf("\n [%d] = %.4f", idxs[i], in[i]);
}
// copy data directly to device memory
cudaMemcpy(d_in, in, num_els * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_idxs, idxs, num_els * sizeof(int),cudaMemcpyHostToDevice);
cudaMemcpy(d_out, out, numBlocks * sizeof(float),cudaMemcpyHostToDevice);
cudaMemcpy(d_oIdxs, oIdxs, numBlocks * sizeof(int),cudaMemcpyHostToDevice);
// warm-up
// reduce<float>(num_els, numThreads, numBlocks, whichKernel, d_in, d_out);
//
// cudaMemcpy(out, d_out, numBlocks * sizeof(float), cudaMemcpyDeviceToHost);
//
// for(int i=0; i< numBlocks; i++)
// printf("\nFinal Result[BLK:%d]: %f", i, out[i]);
// printf("\n Reduce CPU : %f", reduceCPU<float>(in, num_els));
reduceMin<float>(num_els, numThreads, numBlocks, whichKernel, d_in, d_out, d_idxs, d_oIdxs);
cudaMemcpy(out, d_out, numBlocks * sizeof(float), cudaMemcpyDeviceToHost);
cudaMemcpy(oIdxs, d_oIdxs, numBlocks * sizeof(int), cudaMemcpyDeviceToHost);
for(int i=0; i< numBlocks; i++)
printf("\n Reduce MIN GPU idx: %d value: %f", oIdxs[i], out[i]);
int min_idx;
float min;
reduceMINCPU<float>(in, num_els, &min, &min_idx);
printf("\n\n Reduce MIN CPU idx: %d value: %f", min_idx, min);
cudaFree(d_in);
cudaFree(d_out);
cudaFree(d_idxs);
free(in);
free(out);
//system("pause");
return delta;
}
int main(int argc, char* argv[]) {
printf(" GTS250 @ 70.6 GB/s - Finding min and max");
printf("\n N \t\t [GB/s] \t [perc] \t [usec] \t test \n");
//#pragma unroll
//for(int i = 1024*1024; i <= 32*1024*1024; i=i*2)
//{
// my_min_max_test(i);
//}
printf("\n Non-base 2 tests! \n");
printf("\n N \t\t [GB/s] \t [perc] \t [usec] \t test \n");
my_min_max_test(1024);
// just some large numbers....
//my_min_max_test(14*1024*1024+38);
//my_min_max_test(14*1024*1024+55);
//my_min_max_test(18*1024*1024+1232);
//my_min_max_test(7*1024*1024+94854);
//for(int i = 0; i < 4; i++)
//{
//
// float ratio = float(rand())/float(RAND_MAX);
// ratio = ratio >= 0 ? ratio : -ratio;
// int big_num = ratio*18*1e6;
//
// my_min_max_test(big_num);
//}
return 0;
}
答案 0 :(得分：1)
这不是使用动态分配的共享内存的有效方法：
T *sdata = SharedMemory<T>();
int *sdataIdx = SharedMemory<int>();
这两个指针（sdata和sdataIdx）最终都会指向相同的位置。 （documentation讨论了如何正确处理此问题。）
即使您现在想要使用两倍的共享内存（用于存储min plus index），与之前的仅减少代码相比，您还没有增加动态分配大小：
int smemSize =
(threads <= 32) ? 2 * threads * sizeof(T) : threads * sizeof(T);
在您的情况下，threads为256，您为最小存储分配了足够的（threads*sizeof(T)），但没有为索引存储分配任何内容。
我们可以＆＃34;修复＆＃34;通过进行以下更改来解决这些问题：
T *sdata = SharedMemory<T>();
int *sdataIdx = ((int *)sdata) + blockSize;
和
int smemSize =
(threads <= 32) ? 2 * threads * sizeof(T) : threads * sizeof(T);
smemSize += threads*sizeof(int);
通过这些更改，您的代码会产生预期的输出：
$ cuda-memcheck ./t1182
========= CUDA-MEMCHECK
GTS250 @ 70.6 GB/s - Finding min and max
N [GB/s] [perc] [usec] test
Non-base 2 tests!
N [GB/s] [perc] [usec] test
1024 elements
256 threads (max)
Reduce MIN GPU idx: 484 value: 0.001125
Reduce MIN GPU idx: 972 value: 0.002828
Reduce MIN CPU idx: 484 value: 0.001125
========= ERROR SUMMARY: 0 errors
$
（我注释掉了打印出所有1024个初始值的循环）