mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-12-01 11:29:15 +08:00
80 lines
2.9 KiB
Plaintext
80 lines
2.9 KiB
Plaintext
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
|
|
*
|
|
* Redistribution and use in source and binary forms, with or without
|
|
* modification, are permitted provided that the following conditions
|
|
* are met:
|
|
* * Redistributions of source code must retain the above copyright
|
|
* notice, this list of conditions and the following disclaimer.
|
|
* * Redistributions in binary form must reproduce the above copyright
|
|
* notice, this list of conditions and the following disclaimer in the
|
|
* documentation and/or other materials provided with the distribution.
|
|
* * Neither the name of NVIDIA CORPORATION nor the names of its
|
|
* contributors may be used to endorse or promote products derived
|
|
* from this software without specific prior written permission.
|
|
*
|
|
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
|
|
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
|
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
|
|
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
|
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
|
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
|
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
|
|
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
|
|
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
|
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
*/
|
|
|
|
#ifndef MONTECARLO_REDUCTION_CUH
|
|
#define MONTECARLO_REDUCTION_CUH
|
|
|
|
#include <cooperative_groups.h>
|
|
|
|
namespace cg = cooperative_groups;
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// This function calculates total sum for each of the two input arrays.
|
|
// SUM_N must be power of two
|
|
// Unrolling provides a bit of a performance improvement for small
|
|
// to medium path counts.
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <class T, int SUM_N, int blockSize>
|
|
__device__ void sumReduce(T *sum, T *sum2, cg::thread_block &cta,
|
|
cg::thread_block_tile<32> &tile32,
|
|
__TOptionValue *d_CallValue) {
|
|
const int VEC = 32;
|
|
const int tid = cta.thread_rank();
|
|
|
|
T beta = sum[tid];
|
|
T beta2 = sum2[tid];
|
|
T temp, temp2;
|
|
|
|
for (int i = VEC / 2; i > 0; i >>= 1) {
|
|
if (tile32.thread_rank() < i) {
|
|
temp = sum[tid + i];
|
|
temp2 = sum2[tid + i];
|
|
beta += temp;
|
|
beta2 += temp2;
|
|
sum[tid] = beta;
|
|
sum2[tid] = beta2;
|
|
}
|
|
cg::sync(tile32);
|
|
}
|
|
cg::sync(cta);
|
|
|
|
if (tid == 0) {
|
|
beta = 0;
|
|
beta2 = 0;
|
|
for (int i = 0; i < blockDim.x; i += VEC) {
|
|
beta += sum[i];
|
|
beta2 += sum2[i];
|
|
}
|
|
__TOptionValue t = {beta, beta2};
|
|
*d_CallValue = t;
|
|
}
|
|
cg::sync(cta);
|
|
}
|
|
|
|
#endif
|