一种可能的方法是使用以下序列：
thrust::transform- 将输入数据全部转换为 1 或 0：
0 27 42 0 18 99 94 91 0 -- input data
0 1 1 0 1 1 1 1 0 -- this will be our "mask vector"
thrust::inclusive_scan- 将掩码向量转换为渐进序列：
0 1 1 0 1 1 1 1 0 -- "mask" vector
0 1 2 2 3 4 5 6 6 -- "sequence" vector
另一个thrust::transform掩盖非增加值的方法：
0 1 1 0 1 1 1 1 0 -- "mask" vector
0 1 2 2 3 4 5 6 6 -- "sequence" vector
-------------------------
0 1 2 0 3 4 5 6 0 -- result of "AND" operation
请注意，我们可以将前两个步骤与thrust::transform_inclusive_scana 结合起来，然后thrust::transform使用稍有不同的变换函子执行第三步。这种修改允许我们省去创建临时“掩码”向量。
这是一个完整的示例，显示了使用“修改”的方法thrust::transform_inclusive_scan：
$ cat t635.cu
#include <iostream>
#include <stdlib.h>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/transform.h>
#include <thrust/transform_scan.h>
#include <thrust/generate.h>
#include <thrust/copy.h>
#define DSIZE 20
#define PCT_ZERO 40
struct my_unary_op
{
__host__ __device__
int operator()(const int data) const
{
return (!data) ? 0:1;}
};
struct my_binary_op
{
__host__ __device__
int operator()(const int d1, const int d2) const
{
return (!d1) ? 0:d2;}
};
int main(){
// generate DSIZE random 32-bit integers, PCT_ZERO% are zero
thrust::host_vector<int> h_data(DSIZE);
thrust::generate(h_data.begin(), h_data.end(), rand);
for (int i = 0; i < DSIZE; i++)
if ((rand()%100)< PCT_ZERO) h_data[i] = 0;
else h_data[i] %= 1000;
thrust::device_vector<int> d_data = h_data;
thrust::device_vector<int> d_result(DSIZE);
thrust::transform_inclusive_scan(d_data.begin(), d_data.end(), d_result.begin(), my_unary_op(), thrust::plus<int>());
thrust::transform(d_data.begin(), d_data.end(), d_result.begin(), d_result.begin(), my_binary_op());
thrust::copy(d_data.begin(), d_data.end(), std::ostream_iterator<int>(std::cout, ","));
std::cout << std::endl;
thrust::copy(d_result.begin(), d_result.end(), std::ostream_iterator<int>(std::cout, ","));
std::cout << std::endl;
return 0;
}
$ nvcc -o t635 t635.cu
$ ./t635
0,886,777,0,793,0,386,0,649,0,0,0,0,59,763,926,540,426,0,736,
0,1,2,0,3,0,4,0,5,0,0,0,0,6,7,8,9,10,0,11,
$
在我看来，响应更新，这些新信息使问题更难以解决。直方图技术浮现在脑海中，但对 32 位整数（标签）的占用范围没有任何限制，或者对特定标签在数据集中可能重复的次数没有任何限制，直方图技术似乎不切实际。这使我考虑对数据进行排序。
像这样的方法应该有效：
- 用于
thrust::sort对数据进行排序。
- 用于
thrust::unique删除重复项。
- 删除重复的排序数据现在为我们提供了输出集 [0,1,2, ...] 的排序。让我们称之为我们的“地图”。我们可以使用并行二进制搜索技术将原始数据集中的每个标签转换为其映射的输出值。
这个过程对我来说似乎相当“昂贵”。我建议重新考虑上游标记操作，看看是否可以重新设计它以生成更适合高效下游处理的数据集。
无论如何，这是一个完整的示例：
$ cat t635.cu
#include <iostream>
#include <stdlib.h>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/transform.h>
#include <thrust/generate.h>
#include <thrust/sort.h>
#include <thrust/unique.h>
#include <thrust/copy.h>
#define DSIZE 20
#define PCT_ZERO 40
#define RNG 10
#define nTPB 256
// sets idx to the index of the first element in a that is
// equal to or larger than key
__device__ void bsearch_range(const int *a, const int key, const unsigned len_a, unsigned *idx){
unsigned lower = 0;
unsigned upper = len_a;
unsigned midpt;
while (lower < upper){
midpt = (lower + upper)>>1;
if (a[midpt] < key) lower = midpt +1;
else upper = midpt;
}
*idx = lower;
return;
}
__global__ void find_my_idx(const int *a, const unsigned len_a, int *my_data, int *my_idx, const unsigned len_data){
unsigned idx = (blockDim.x * blockIdx.x) + threadIdx.x;
if (idx < len_data){
unsigned sp_a;
int val = my_data[idx];
bsearch_range(a, val, len_a, &sp_a);
my_idx[idx] = sp_a;
}
}
int main(){
// generate DSIZE random 32-bit integers, PCT_ZERO% are zero
thrust::host_vector<int> h_data(DSIZE);
thrust::generate(h_data.begin(), h_data.end(), rand);
for (int i = 0; i < DSIZE; i++)
if ((rand()%100)< PCT_ZERO) h_data[i] = 0;
else h_data[i] %= RNG;
thrust::device_vector<int> d_data = h_data;
thrust::device_vector<int> d_result = d_data;
thrust::sort(d_result.begin(), d_result.end());
thrust::device_vector<int> d_unique = d_result;
int unique_size = thrust::unique(d_unique.begin(), d_unique.end()) - d_unique.begin();
find_my_idx<<< (DSIZE+nTPB-1)/nTPB , nTPB >>>(thrust::raw_pointer_cast(d_unique.data()), unique_size, thrust::raw_pointer_cast(d_data.data()), thrust::raw_pointer_cast(d_result.data()), DSIZE);
thrust::copy(d_data.begin(), d_data.end(), std::ostream_iterator<int>(std::cout, ","));
std::cout << std::endl;
thrust::copy(d_result.begin(), d_result.end(), std::ostream_iterator<int>(std::cout, ","));
std::cout << std::endl;
return 0;
}
$ nvcc t635.cu -o t635
$ ./t635
0,6,7,0,3,0,6,0,9,0,0,0,0,9,3,6,0,6,0,6,
0,2,3,0,1,0,2,0,4,0,0,0,0,4,1,2,0,2,0,2,
$
