Istvan Kiss
197f73dac9
* Add examples to tools folder * Correct P2P memory access section * Sync poriting guide * Add HIP Graph tutorial * Add hint about using amdgpu-dkms for IPC API * Add a few more env variables
111 行
3.2 KiB
Plaintext
111 行
3.2 KiB
Plaintext
// MIT License
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//
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// Copyright (c) 2025 Advanced Micro Devices, Inc. All rights reserved.
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//
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// Permission is hereby granted, free of charge, to any person obtaining a copy
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// of this software and associated documentation files (the "Software"), to deal
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// in the Software without restriction, including without limitation the rights
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// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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// copies of the Software, and to permit persons to whom the Software is
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// furnished to do so, subject to the following conditions:
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//
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// The above copyright notice and this permission notice shall be included in all
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// copies or substantial portions of the Software.
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//
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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// SOFTWARE.
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// [sphinx-start]
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#include <cuda_runtime.h>
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#include <iostream>
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#include <vector>
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__global__ void block_reduction(const float* input, float* output, int num_elements)
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{
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extern __shared__ float s_data[];
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int tid = threadIdx.x;
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int global_id = blockDim.x * blockIdx.x + tid;
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if (global_id < num_elements)
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{
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s_data[tid] = input[global_id];
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}
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else
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{
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s_data[tid] = 0.0f;
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}
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__syncthreads();
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for (int stride = blockDim.x / 2; stride > 0; stride >>= 1)
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{
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if (tid < stride)
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{
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s_data[tid] += s_data[tid + stride];
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}
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__syncthreads();
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}
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if (tid == 0)
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{
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output[blockIdx.x] = s_data[0];
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}
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}
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int main()
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{
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int threads = 256;
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const int num_elements = 50000;
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std::vector<float> h_a(num_elements);
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std::vector<float> h_b((num_elements + threads - 1) / threads);
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for (int i = 0; i < num_elements; ++i)
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{
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h_a[i] = rand() / static_cast<float>(RAND_MAX);
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}
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float *d_a, *d_b;
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cudaMalloc(&d_a, h_a.size() * sizeof(float));
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cudaMalloc(&d_b, h_b.size() * sizeof(float));
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cudaStream_t stream;
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cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking);
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cudaEvent_t start_event, stop_event;
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cudaEventCreate(&start_event);
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cudaEventCreate(&stop_event);
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cudaMemcpyAsync(d_a, h_a.data(), h_a.size() * sizeof(float), cudaMemcpyHostToDevice, stream);
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cudaEventRecord(start_event, stream);
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int blocks = (num_elements + threads - 1) / threads;
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block_reduction<<<blocks, threads, threads * sizeof(float), stream>>>(d_a, d_b, num_elements);
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cudaMemcpyAsync(h_b.data(), d_b, h_b.size() * sizeof(float), cudaMemcpyDeviceToHost, stream);
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cudaEventRecord(stop_event, stream);
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cudaEventSynchronize(stop_event);
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float milliseconds = 0.f;
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cudaEventElapsedTime(&milliseconds, start_event, stop_event);
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std::cout << "Kernel execution time: " << milliseconds << " ms\n";
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cudaFree(d_a);
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cudaFree(d_b);
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cudaEventDestroy(start_event);
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cudaEventDestroy(stop_event);
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cudaStreamDestroy(stream);
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return 0;
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}
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// [sphinx-end]
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