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
81 lines
2.4 KiB
Plaintext
81 lines
2.4 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) 2022-2025 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 Compute square roots via Newton's method using while_graph_scope
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*
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* This is a minimal example of an iterative solver with convergence
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* checking in a stackable context. Each iteration applies the
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* Babylonian step x <- (x + S/x) / 2 and reduces the maximum
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* absolute change across all elements. The while loop exits once
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* the change drops below a tolerance.
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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 main()
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{
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#if _CCCL_CTK_BELOW(12, 4)
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fprintf(stderr, "Waiving example: while_graph_scope requires CUDA 12.4+.\n");
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return 0;
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#else
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stackable_ctx ctx;
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constexpr size_t N = 1024;
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constexpr double tol = 1e-12;
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::std::vector<double> host_S(N);
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::std::vector<double> host_X(N);
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for (size_t i = 0; i < N; i++)
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{
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host_S[i] = 1.0 + static_cast<double>(i);
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host_X[i] = host_S[i]; // initial guess x0 = S
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}
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auto lS = ctx.logical_data(make_slice(host_S.data(), N)).set_symbol("S");
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lS.set_read_only();
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auto lX = ctx.logical_data(make_slice(host_X.data(), N)).set_symbol("X");
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auto lmax_err = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("max_err");
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{
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auto while_guard = ctx.while_graph_scope();
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// Babylonian step: x = (x + S/x) / 2, reduce max |change|
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ctx.parallel_for(box(N), lX.rw(), lS.read(), lmax_err.reduce(reducer::maxval<double>{}))
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->*[] __device__(size_t i, auto x, auto s, auto& max_err) {
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double x_old = x(i);
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double x_new = 0.5 * (x_old + s(i) / x_old);
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x(i) = x_new;
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max_err = fabs(x_new - x_old);
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};
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while_guard.update_cond(lmax_err.read())->*[tol] __device__(auto max_err) {
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return (*max_err > tol);
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};
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}
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ctx.finalize();
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for (size_t i = 0; i < N; i++)
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{
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double expected = sqrt(1.0 + static_cast<double>(i));
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EXPECT(fabs(host_X[i] - expected) < 1e-8);
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}
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return 0;
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#endif
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}
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