我有一个单词列表，我的目标是匹配一个非常长的短语中的每个单词.我在匹配每个单词时没有问题，我唯一的问题是返回一个包含每个匹配信息的结构向量.
I have a list of words, my goal is to match each word in a very very long phrase. I'm having no problem in matching each word, my only problem is to return a vector of structures containing informations about each match.
在代码中:
typedef struct {
int A, B, C; } Match;
__global__ void Find(veryLongPhrase * _phrase, Words * _word_list, vector<Match> * _matches)
{
int a, b, c;
[...] //Parallel search for each word in the phrase
if(match) //When an occurrence is found
{
_matches.push_back(new Match{ A = a, B = b, C = c }); //Here comes the unknown, what should I do here???
}
}
main()
{
[...]
veryLongPhrase * myPhrase = "The quick brown fox jumps over the lazy dog etc etc etc..."
Words * wordList = {"the", "lazy"};
vector<Match> * matches; //Obviously I can't pass a vector to a kernel
Find<<< X, Y >>>(myPhrase, wordList, matches);
[...]
}
我已经尝试过 Thrust 库，但没有成功，你能给我建议任何类型的解决方案吗?
I have tried Thrust library but without any success, can you suggest me any kind of solution?
非常感谢.
这样的事情应该可以工作(在浏览器中编码，未测试):
something like this should work (coded in browser, not tested):
// N is the maximum number of structs to insert
#define N 10000
typedef struct {
int A, B, C; } Match;
__device__ Match dev_data[N];
__device__ int dev_count = 0;
__device__ int my_push_back(Match * mt) {
int insert_pt = atomicAdd(&dev_count, 1);
if (insert_pt < N){
dev_data[insert_pt] = *mt;
return insert_pt;}
else return -1;}
__global__ void Find(veryLongPhrase * _phrase, Words * _word_list, vector<Match> * _matches)
{
int a, b, c;
[...] //Parallel search for each word in the phrase
if(match) //When an occurrence is found
{
my_push_back(new Match{ A = a, B = b, C = c }); }
}
main()
{
[...]
veryLongPhrase * myPhrase = "The quick brown fox jumps over the lazy dog etc etc etc..."
Words * wordList = {"the", "lazy"};
Find<<< X, Y >>>(myPhrase, wordList);
int dsize;
cudaMemcpyFromSymbol(&dsize, dev_count, sizeof(int));
vector<Match> results(dsize);
cudaMemcpyFromSymbol(&(results[0]), dev_data, dsize*sizeof(Match));
[...]
}
这将需要原子操作的计算能力 1.1 或更高.
This will require compute capability 1.1 or better for the atomic operation.
nvcc -arch=sm_11 ...
这是一个有效的例子:
$ cat t347.cu
#include <iostream>
#include <vector>
// N is the maximum number of structs to insert
#define N 10000
typedef struct {
int A, B, C; } Match;
__device__ Match dev_data[N];
__device__ int dev_count = 0;
__device__ int my_push_back(Match & mt) {
int insert_pt = atomicAdd(&dev_count, 1);
if (insert_pt < N){
dev_data[insert_pt] = mt;
return insert_pt;}
else return -1;}
__global__ void Find()
{
if(threadIdx.x < 10) //Simulate a found occurrence
{
Match a = { .A = 1, .B = 2, .C = 3 };
my_push_back(a); }
}
main()
{
Find<<< 2, 256 >>>();
int dsize;
cudaMemcpyFromSymbol(&dsize, dev_count, sizeof(int));
if (dsize >= N) {printf("overflow error
"); return 1;}
std::vector<Match> results(dsize);
cudaMemcpyFromSymbol(&(results[0]), dev_data, dsize*sizeof(Match));
std::cout << "number of matches = " << dsize << std::endl;
std::cout << "A = " << results[dsize-1].A << std:: endl;
std::cout << "B = " << results[dsize-1].B << std:: endl;
std::cout << "C = " << results[dsize-1].C << std:: endl;
}
$ nvcc -arch=sm_11 -o t347 t347.cu
$ ./t347
number of matches = 20
A = 1
B = 2
C = 3
$
注意，在这种情况下，我的Match结果结构创建是不同的，我是通过引用传递的，但概念是一样的.
Note that in this case my Match result struct creation is different, and I am passing by reference, but the concept is the same.
这篇关于Cuda 内核返回向量的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！