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
76 lines
1.7 KiB
Plaintext
76 lines
1.7 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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*
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* @brief An example of Fibonacci sequence illustrating how we can use
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* dynamically created logical data
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*/
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#include <cuda/experimental/stf.cuh>
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using namespace cuda::experimental::stf;
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int fibo_ref(int n)
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{
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if (n < 2)
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{
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return n;
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}
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else
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{
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return fibo_ref(n - 1) + fibo_ref(n - 2);
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}
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}
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__global__ void add(slice<int> out, const slice<const int> in1, const slice<const int> in2)
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{
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out(0) = in1(0) + in2(0);
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}
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__global__ void set(slice<int> out, int val)
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{
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out(0) = val;
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}
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logical_data<slice<int>> compute_fibo(context& ctx, int n)
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{
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auto out = ctx.logical_data(shape_of<slice<int>>(1));
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if (n < 2)
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{
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ctx.task(out.write())->*[=](cudaStream_t s, auto sout) {
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set<<<1, 1, 0, s>>>(sout, n);
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};
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}
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else
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{
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auto fib1 = compute_fibo(ctx, n - 1);
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auto fib2 = compute_fibo(ctx, n - 2);
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ctx.task(fib1.read(), fib2.read(), out.write())->*[=](cudaStream_t s, auto s1, auto s2, auto sout) {
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add<<<1, 1, 0, s>>>(sout, s1, s2);
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};
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}
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return out;
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}
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int main(int argc, char** argv)
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{
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int n = (argc > 1) ? atoi(argv[1]) : 4;
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context ctx; // = graph_ctx();
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auto result = compute_fibo(ctx, n);
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ctx.host_launch(result.read())->*[&](auto res) {
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EXPECT(res(0) == fibo_ref(n));
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};
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ctx.finalize();
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}
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