dedf08166a
Added 863 files from NVIDIA/cccl sparse checkout: - c2h/ (27 files): Catch2 test helpers — generators, validators, runner - nvbench_helper/ (10 files): Benchmark harness utilities - cmake/ (29 files): CMake presets and build helpers - cudax/ (794 files): Experimental CUDA extensions - AGENTS.md: NVIDIA's official AI agent instructions for CCCL - CMakePresets.json: Standardized build configurations - cccl-version.json: Version tracking Also added CCCL_ASSET_MAP.md mapping all 4295 CCCL files to competition value and PRD items. cccl_upstream now covers 100% of competition-critical assets: - 27 tuning headers (SM80/90/100 benchmark data) - 32 dispatch headers (algorithm implementations) - 60 Thrust examples (correctness verification) - 217 CUB Catch2 tests (regression matrix) - 153 CUB benchmarks (parameter space search) - 18 CUB examples (API verification) - 27 test helpers + benchmark harness - 794 cudax experimental extensions
70 lines
2.0 KiB
Plaintext
70 lines
2.0 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 This example illustrates how we can create temporary data from shapes, and use them in tasks
|
|
*/
|
|
|
|
#include <cuda/experimental/stf.cuh>
|
|
|
|
using namespace cuda::experimental::stf;
|
|
|
|
int main()
|
|
{
|
|
const int n = 4096;
|
|
int X[n];
|
|
int Y[n];
|
|
|
|
for (size_t i = 0; i < n; i++)
|
|
{
|
|
X[i] = 3 * i;
|
|
Y[i] = 2 * i - 3;
|
|
}
|
|
|
|
context ctx;
|
|
|
|
auto lX = ctx.logical_data(X);
|
|
auto lY = ctx.logical_data(Y);
|
|
|
|
// Select an odd number
|
|
int niter = 19;
|
|
assert(niter % 2 == 1);
|
|
|
|
for (int iter = 0; iter < niter; iter++)
|
|
{
|
|
// We here define a temporary vector with the same shape as X, for which there is no existing copy
|
|
// This data handle has a limited scope, so that it is automatically destroyed at each iteration of the loop
|
|
auto tmp = ctx.logical_data(lX.shape());
|
|
|
|
ctx.task(lY.rw(), lX.rw(), tmp.write())->*[](cudaStream_t s, auto sY, auto sX, auto sTMP) {
|
|
// We swap X and Y using TMP as temporary buffer
|
|
// TMP = X
|
|
cuda_safe_call(
|
|
cudaMemcpyAsync(sTMP.data_handle(), sX.data_handle(), n * sizeof(int), cudaMemcpyDeviceToDevice, s));
|
|
// X = Y
|
|
cuda_safe_call(cudaMemcpyAsync(sX.data_handle(), sY.data_handle(), n * sizeof(int), cudaMemcpyDeviceToDevice, s));
|
|
// Y = TMP
|
|
cuda_safe_call(
|
|
cudaMemcpyAsync(sY.data_handle(), sTMP.data_handle(), n * sizeof(int), cudaMemcpyDeviceToDevice, s));
|
|
};
|
|
}
|
|
|
|
ctx.finalize();
|
|
|
|
// We have exchanged an odd number of times, so they must be inverted
|
|
for (size_t i = 0; i < n; i++)
|
|
{
|
|
assert(X[i] == 2 * i - 3);
|
|
assert(Y[i] == 3 * i);
|
|
}
|
|
}
|