1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
|
/*
* SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
This is a simple example demonstrating the use of CCCL functionality from Thrust, CUB, and libcu++.
The example computes the sum of an array of integers using a simple parallel reduction. Each thread block
computes the sum of a subset of the array using cuB::BlockRecuce. The sum of each block is then reduced
to a single value using an atomic add via cuda::atomic_ref from libcu++. The result is stored in a device_vector
from Thrust. The sum is then printed to the console.
*/
#include <cub/block/block_reduce.cuh>
#include <thrust/device_vector.h>
#include <cuda/atomic>
#include <cstdio>
constexpr int block_size = 256;
__global__ void sumKernel(int const* data, int* result, std::size_t N)
{
using BlockReduce = cub::BlockReduce<int, block_size>;
__shared__ typename BlockReduce::TempStorage temp_storage;
int index = threadIdx.x + blockIdx.x * blockDim.x;
int sum = 0;
if (index < N)
{
sum += data[index];
}
sum = BlockReduce(temp_storage).Sum(sum);
if (threadIdx.x == 0)
{
cuda::atomic_ref<int, cuda::thread_scope_device> atomic_result(*result);
atomic_result.fetch_add(sum, cuda::memory_order_relaxed);
}
}
int main()
{
std::size_t N = 1000;
thrust::device_vector<int> data(N, 1);
thrust::device_vector<int> result(1);
int num_blocks = (N + block_size - 1) / block_size;
sumKernel<<<num_blocks, block_size>>>(
thrust::raw_pointer_cast(data.data()), thrust::raw_pointer_cast(result.data()), N);
auto err = cudaDeviceSynchronize();
if (err != cudaSuccess)
{
std::cout << "Error: " << cudaGetErrorString(err) << std::endl;
return -1;
}
std::cout << "Sum: " << result[0] << std::endl;
assert(result[0] == N);
return 0;
}
|
