56fd68e7dd
CCCL (CUDA C++ Core Libraries) provides: - CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk) - Thrust: high-level parallel algorithms (transform_reduce, sort, scan) - libcudacxx: CUDA C++ standard library (atomics, barriers, memory) - cudax: experimental features (memory resources, allocators) - Tuning policies: per-SM hardware-specific algorithm parameters Competition optimization vectors mapped to CCCL: - Output TPS (83% weight): warp_reduce, block_reduce, device_topk - Input TPS (14% weight): device_scan, block_load, prefetch - Cache TPS (3% weight): prefix caching strategy patterns - Memory (0.9 util): pooled/cached/buddy allocators Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only) License: Apache-2.0
87 lines
2.3 KiB
Plaintext
87 lines
2.3 KiB
Plaintext
//===----------------------------------------------------------------------===//
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//
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// Part of CUDASTF in CUDA C++ Core Libraries,
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// under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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// SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
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//
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//===----------------------------------------------------------------------===//
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/**
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* @file
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* @brief Ensure we can use the same logical data multiple time in a task
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*/
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#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
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#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
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using namespace cuda::experimental::stf;
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template <typename T>
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__global__ void diff_cnt(int n, T* x, T* y, int* delta)
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{
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int tid = blockIdx.x * blockDim.x + threadIdx.x;
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int nthreads = gridDim.x * blockDim.x;
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for (int ind = tid; ind < n; ind += nthreads)
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{
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if (y[ind] != x[ind])
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{
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atomicAdd(delta, 1);
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}
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}
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}
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template <typename Ctx, typename T>
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void compare_two_vectors(Ctx& ctx, logical_data<T>& a, logical_data<T>& b, int& delta)
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{
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auto delta_cnt = ctx.logical_data(make_slice(&delta, 1));
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const auto n = a.shape().extent(0);
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// Count the number of differences
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ctx.task(a.read(), b.read(), delta_cnt.rw())->*[=](cudaStream_t stream, auto da, auto db, auto ddelta) {
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diff_cnt<<<16, 128, 0, stream>>>(static_cast<int>(n), da.data_handle(), db.data_handle(), ddelta.data_handle());
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};
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// Read that value on the host
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ctx.host_launch(delta_cnt.read())->*[&](auto /*unused*/) {};
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}
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static const size_t N = 12;
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template <class Ctx>
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void run(double (&X)[N], double (&Y)[N])
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{
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Ctx ctx;
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auto handle_X = ctx.logical_data(X);
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auto handle_Y = ctx.logical_data(Y);
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int ret1 = 0, ret2 = 0;
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compare_two_vectors(ctx, handle_X, handle_Y, ret1);
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compare_two_vectors(ctx, handle_X, handle_X, ret2);
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ctx.finalize();
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// After sync, we can inspect the returned values.
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// First two vectors are different
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assert(ret1 > 0);
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// Other two vectors are equal
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assert(ret2 == 0);
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}
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int main()
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{
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double X[N], Y[N];
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for (size_t ind = 0; ind < N; ind++)
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{
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X[ind] = 1.0 * ind;
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Y[ind] = 2.0 * ind - 3.0;
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}
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run<stream_ctx>(X, Y);
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run<graph_ctx>(X, Y);
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}
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