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b7c5481c55
This is the release of the CUDA 13.3 samples, which include additions for CUDA Tile C++, and updated CCCL and Python samples.
137 lines
5.0 KiB
Plaintext
137 lines
5.0 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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* This sample demonstrates a simple vector addition using CUDA Tile C++.
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* The vector addition is performed by splitting the dataset into blocks
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* which process 1024 elements at a time. The cuda::tiles::partition_view
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* type is used to partition the data into chunks of size 1024. Each block loads
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* its respective chunk from 'a' and 'b', performs an elementwise addition,
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* then stores it to the corresponding chunk of 'c'. Masked loads and stores
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* are used to ensure that the last chunk which is partially out of bounds is
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* correctly handled.
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*
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* A SIMT kernel is used to initialize the input vectors.
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*/
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#include "helper_cuda.h"
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#include "cuda_tile.h"
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#include "cuda_fp16.h"
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#include <cstdio>
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__global__ void initializeVectors(__half* a, __half* b, std::size_t n) {
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auto idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx < n) {
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a[idx] = __half{0.5 * idx};
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b[idx] = __half{1.5 * idx};
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}
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}
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/* Declares a tile kernel with '__restrict__' pointers (important for performance) */
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__tile_global__ void vectorAdd(__half* __restrict__ a,
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__half* __restrict__ b,
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__half* __restrict__ c,
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std::size_t n) {
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/* set up the namespace */
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namespace ct = cuda::tiles;
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using namespace ct::literals;
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/* indicate to the compiler that the pointers are aligned (important for optimizations) */
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a = ct::assume_aligned(a, 16_ic);
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b = ct::assume_aligned(b, 16_ic);
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c = ct::assume_aligned(c, 16_ic);
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/* get the block index in the x dimension */
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auto idx = ct::bid().x;
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/* create tensor spans representing arrays of length 'n' based on the points 'a', 'b', and 'c' */
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ct::tensor_span a_span{a, ct::extents{n}};
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ct::tensor_span b_span{b, ct::extents{n}};
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ct::tensor_span c_span{c, ct::extents{n}};
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/* create partition views over the full arrays, partitioned into chunks of 1024 */
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auto view_a = ct::partition_view{a_span, ct::shape{1024_ic}};
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auto view_b = ct::partition_view{b_span, ct::shape{1024_ic}};
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auto view_c = ct::partition_view{c_span, ct::shape{1024_ic}};
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/* load the tiles from the input partitions */
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auto tile_a = view_a.load_masked(idx);
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auto tile_b = view_b.load_masked(idx);
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/* add the tiles together, elementwise */
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auto tile_c = tile_a + tile_b;
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/* store the result tile to the output partition */
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view_c.store_masked(tile_c, idx);
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}
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int main() {
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__half* d_a = nullptr;
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__half* d_b = nullptr;
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__half* d_c = nullptr;
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int N = 8000;
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int chunk_size = 1024;
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int num_blocks = 1 + ((N - 1) / chunk_size);
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checkCudaErrors(cudaMalloc(&d_a, N * sizeof(__half)));
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checkCudaErrors(cudaMalloc(&d_b, N * sizeof(__half)));
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checkCudaErrors(cudaMalloc(&d_c, N * sizeof(__half)));
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initializeVectors<<<num_blocks, chunk_size>>>(d_a, d_b, N);
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checkCudaErrors(cudaGetLastError());
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vectorAdd<<<num_blocks>>>(d_a, d_b, d_c, N);
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checkCudaErrors(cudaGetLastError());
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checkCudaErrors(cudaDeviceSynchronize());
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__half* h_c = new __half[N];
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checkCudaErrors(cudaMemcpy(h_c, d_c, N * sizeof(__half), cudaMemcpyDeviceToHost));
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for (int idx = 0; idx != N; ++idx) {
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if (h_c[idx] != __half{2 * idx}) {
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printf("Expected: h_c[%i] == %i\n", idx, 2 * idx);
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printf("Actual: h_c[%i] == %f\n", idx, float(h_c[idx]));
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return 1;
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}
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}
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printf("Success! Vector addition matches expected results.\n");
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checkCudaErrors(cudaFree(d_a));
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checkCudaErrors(cudaFree(d_b));
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checkCudaErrors(cudaFree(d_c));
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delete[] h_c;
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}
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