74 lines
1.8 KiB
Plaintext
74 lines
1.8 KiB
Plaintext
|
//===----------------------------------------------------------------------===//
|
||
|
|
//
|
||
|
|
// Part of CUDASTF in CUDA C++ Core Libraries,
|
||
|
|
// under the Apache License v2.0 with LLVM Exceptions.
|
||
|
|
// See https://llvm.org/LICENSE.txt for license information.
|
||
|
|
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
|
||
|
|
// SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
|
||
|
|
//
|
||
|
|
//===----------------------------------------------------------------------===//
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @file
|
||
|
|
* @brief A reduction kernel written using launch and CUB
|
||
|
|
*/
|
||
|
|
|
||
|
|
#include <cub/cub.cuh>
|
||
|
|
|
||
|
|
#include <cuda/experimental/stf.cuh>
|
||
|
|
|
||
|
|
using namespace cuda::experimental::stf;
|
||
|
|
|
||
|
|
double X0(int i)
|
||
|
|
{
|
||
|
|
return sin((double) i);
|
||
|
|
}
|
||
|
|
|
||
|
|
int main()
|
||
|
|
{
|
||
|
|
context ctx;
|
||
|
|
|
||
|
|
const size_t N = 128 * 1024 * 1024;
|
||
|
|
|
||
|
|
std::vector<double> X(N);
|
||
|
|
double sum = 0.0;
|
||
|
|
|
||
|
|
double ref_sum = 0.0;
|
||
|
|
|
||
|
|
for (size_t ind = 0; ind < N; ind++)
|
||
|
|
{
|
||
|
|
X[ind] = sin((double) ind);
|
||
|
|
ref_sum += X[ind];
|
||
|
|
}
|
||
|
|
|
||
|
|
auto lX = ctx.logical_data(&X[0], {N});
|
||
|
|
auto lsum = ctx.logical_data(&sum, {1});
|
||
|
|
|
||
|
|
auto number_devices = 2;
|
||
|
|
auto where = exec_place::repeat(exec_place::device(0), number_devices);
|
||
|
|
|
||
|
|
auto spec = par<32>(con<128>());
|
||
|
|
|
||
|
|
ctx.launch(spec, where, lX.read(), lsum.rw())->*[] _CCCL_DEVICE(auto th, auto x, auto sum) {
|
||
|
|
// Each thread computes the sum of elements assigned to it
|
||
|
|
double local_sum = 0.0;
|
||
|
|
for (auto ind : th.apply_partition(shape(x)))
|
||
|
|
{
|
||
|
|
local_sum += x(ind);
|
||
|
|
}
|
||
|
|
|
||
|
|
using BlockReduce = cub::BlockReduce<double, th.static_width(1)>;
|
||
|
|
__shared__ typename BlockReduce::TempStorage temp_storage;
|
||
|
|
|
||
|
|
double block_sum = BlockReduce(temp_storage).Sum(local_sum);
|
||
|
|
if (th.inner().rank() == 0)
|
||
|
|
{
|
||
|
|
atomicAdd(&sum(0), block_sum);
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
ctx.finalize();
|
||
|
|
|
||
|
|
EXPECT(fabs(sum - ref_sum) < 0.0001);
|
||
|
|
}
|