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5443602d89
Release 13.4 of the CUDA Samples supported by CUDA Toolkit 13.4. See Changelog for more information.
107 lines
4.4 KiB
Plaintext
107 lines
4.4 KiB
Plaintext
/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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/*
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* Shared code for the simpleCudaGraphs sample. Both the explicit-graph and
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* stream-capture demos build a graph around the same two-pass reduction, so the
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* reduction kernels, the host callback and its data type, and the input-fill
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* helper live here to avoid duplicating them across the two .cu files.
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*/
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#pragma once
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#include <cub/cub.cuh>
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#include <cuda_runtime.h>
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#include <cstdio>
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#include <cstdlib>
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#define THREADS_PER_BLOCK 512
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// Payload passed to the host-callback node: the demo name and the reduced result.
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typedef struct callBackData
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{
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const char *fn_name;
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double *data;
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} callBackData_t;
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// Host-callback node body: prints the reduced result, then resets it for the next launch.
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inline void CUDART_CB myHostNodeCallback(void *data)
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{
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callBackData_t *tmp = (callBackData_t *)(data);
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double *result = (double *)(tmp->data);
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printf("[%s] Host callback final reduced sum = %lf\n", tmp->fn_name, *result);
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*result = 0.0; // reset the result
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}
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// Fill the host input buffer with fresh pseudo-random values. Called before each
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// graph launch so the same graph processes different data every iteration.
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inline void init_input(float *inputVec, size_t size)
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{
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for (size_t i = 0; i < size; i++)
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inputVec[i] = (rand() & 0xFF) / (float)RAND_MAX;
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}
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// Pass 1: reduce the input float vector to one partial sum per block.
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__global__ void reduce(float *inputVec, double *outputVec, size_t inputSize, size_t outputSize)
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{
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typedef cub::BlockReduce<double, THREADS_PER_BLOCK> BlockReduceT;
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__shared__ typename BlockReduceT::TempStorage temp_storage;
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size_t globaltid = blockIdx.x * blockDim.x + threadIdx.x;
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// Each thread adds its assigned elements into a local partial sum
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double temp_sum = 0.0;
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for (size_t i = globaltid; i < inputSize; i += (size_t)gridDim.x * blockDim.x)
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temp_sum += (double)inputVec[i];
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// CUB reduces all per-thread partial sums to a single block sum; result lands on thread 0
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double block_sum = BlockReduceT(temp_storage).Sum(temp_sum);
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if (threadIdx.x == 0 && blockIdx.x < outputSize)
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outputVec[blockIdx.x] = block_sum;
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}
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// Pass 2: reduce the per-block partial sums to a single scalar result.
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__global__ void reduceFinal(double *inputVec, double *result, size_t inputSize)
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{
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typedef cub::BlockReduce<double, THREADS_PER_BLOCK> BlockReduceT;
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__shared__ typename BlockReduceT::TempStorage temp_storage;
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size_t globaltid = blockIdx.x * blockDim.x + threadIdx.x;
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// Each thread adds its assigned elements into a local partial sum
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double temp_sum = 0.0;
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for (size_t i = globaltid; i < inputSize; i += (size_t)gridDim.x * blockDim.x)
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temp_sum += (double)inputVec[i];
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// CUB reduces all per-thread partial sums to a single block sum; result lands on thread 0
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double block_sum = BlockReduceT(temp_storage).Sum(temp_sum);
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if (threadIdx.x == 0)
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result[0] = block_sum;
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}
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