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最新推荐文章于 2024-12-05 20:25:47 发布

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本文介绍了一种利用CUDA技术在GPU上实现的并行快速排序算法，通过共享内存优化，显著提高了排序效率。

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#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "device_functions.h"
#include <iostream>
#include <stdio.h>
using namespace std;

#define u32 unsigned int

__global__ void gpu_sort_array_array(
u32 * const data,
const u32 num_lists,
const u32 num_elements);

__device__ void copy_data_to_shared(
const u32 * const data,
u32 * const sort_tmp,
const u32 num_lists,
const u32 num_elements,
const u32 tid);

__device__ void radix_sort2(
u32 * const sort_tmp,
const u32 num_lists,
const u32 num_elements,
const u32 tid,
u32 * const sort_tmp_0);

__device__ void merge_array6(const u32 *const src_array,
u32 * const dest_array,
const u32 num_lists,
const u32 num_elements,
const u32 tid);

const int MAX_NUM_LISTS = 100, NUM_ELEM=1;

int main()
{
return 0;
}

__global__ void gpu_sort_array_array(
u32 * const data,
const u32 num_lists,
const u32 num_elements)
{
const u32 tid = (blockIdx.x*blockDim.x) + threadIdx.x;
__shared__ u32 sort_tmp_0[NUM_ELEM];
__shared__ u32 sort_tmp_1[NUM_ELEM];
copy_data_to_shared(data, sort_tmp_0, num_lists, num_elements, tid);
radix_sort2(sort_tmp_0, num_lists, num_elements, tid, sort_tmp_1);
merge_array6(sort_tmp_0, data, num_lists, num_elements, tid);
}

__device__ void copy_data_to_shared(
const u32 * const data,
u32 * const sort_tmp_0,
const u32 num_lists,
const u32 num_elements,
const u32 tid)
{
for (u32 i = 0; i < num_elements; i += num_lists)
{
sort_tmp_0[i + tid] = data[i + tid];
}
__syncthreads();
}

__device__ void radix_sort2(
u32 * const sort_tmp,
const u32 num_lists,
const u32 num_elements,
const u32 tid,
u32 * const sort_tmp_0)
{
for (u32 bit = 0; bit < 32; bit++)
{
const u32 bit_mask = (1 << bit);
u32 base_cnt_0 = 0;
u32 base_cnt_1 = 0;
for (u32 i = 0; i < num_elements; i += num_lists)
{
const u32 elem = sort_tmp[i + tid];
if ((elem&bit_mask)>0)
{
sort_tmp_0[base_cnt_1 + tid] = elem;
base_cnt_1 += num_lists;
}
else
{
sort_tmp[base_cnt_0 + tid] = elem;
base_cnt_0 += num_lists;
}
}

for (u32 i = 0; i < base_cnt_0; i += num_lists)
{
sort_tmp[base_cnt_0 + i + tid] = sort_tmp_0[i + tid];
}
}
__syncthreads();
}

__device__ void merge_array6(const u32 *const src_array,
u32 * const dest_array,
const u32 num_lists,
const u32 num_elements,
const u32 tid)
{
const u32 num_elements_per_list = (num_elements / num_lists);
__shared__ u32 list_indexes[MAX_NUM_LISTS];
list_indexes[tid] = 0;
__syncthreads();
for (u32 i = 0; i < num_elements; i++)
{
__shared__ u32 min_val;
__shared__ u32 min_tid;
u32 data;
if (list_indexes[tid] < num_elements_per_list)
{
const u32 src_idx = tid + (list_indexes[tid] * num_lists);
data = src_array[src_idx];
}
else
{
data = 0xFFFFFFFF;
}
if (tid == 0)
{
min_val = 0xFFFFFFFF;
min_tid = 0xFFFFFFFF;
}
__syncthreads();
__uAtomicMin(&min_val, data);
__syncthreads();
if (min_val == data)
{
__uAtomicMin(&min_tid, tid);
}
__syncthreads();
if (tid == min_tid)
{
list_indexes[tid]++;
dest_array[i] == data;
}
}
}