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
106 lines
3.0 KiB
Plaintext
106 lines
3.0 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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* @brief Demonstration of graph_scope RAII usage styles
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*
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* This example shows different ways to use stackable_ctx::graph_scope_guard
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* for automatic push/pop management in nested contexts.
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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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stackable_ctx ctx;
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int data[10] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
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auto lA = ctx.logical_data(data);
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// Style 1: Direct constructor (like std::lock_guard)
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// This is the most idiomatic C++ style
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{
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stackable_ctx::graph_scope_guard scope{ctx}; // Direct constructor - push() called
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auto temp = ctx.logical_data(lA.shape());
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ctx.parallel_for(temp.shape(), temp.write(), lA.read())->*[] __device__(size_t i, auto temp, auto a) {
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temp(i) = a(i) * 2;
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};
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ctx.parallel_for(lA.shape(), lA.write(), temp.read())->*[] __device__(size_t i, auto a, auto temp) {
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a(i) = temp(i);
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};
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// pop() called automatically when scope goes out of scope
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}
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// Style 2: Factory method (convenience)
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// Useful when you prefer auto type deduction
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{
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auto scope = ctx.graph_scope(); // Factory method - push() called
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ctx.parallel_for(lA.shape(), lA.rw())->*[] __device__(size_t i, auto a) {
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a(i) += 1;
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};
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// pop() called automatically
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}
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// Style 3: Direct constructor with explicit type alias
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// Useful for readability in complex scenarios
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{
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using scope_t = stackable_ctx::graph_scope_guard;
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scope_t scope{ctx}; // Explicit type - push() called
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ctx.parallel_for(lA.shape(), lA.rw())->*[] __device__(size_t i, auto a) {
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a(i) *= 3;
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};
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// pop() called automatically
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}
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// Style 4: Iterative pattern (like in stackable2.cu)
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// Demonstrates repeated nested contexts
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for (int iter = 0; iter < 3; iter++)
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{
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stackable_ctx::graph_scope_guard iteration{ctx}; // New scope each iteration
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auto temp = ctx.logical_data(lA.shape());
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// tmp = a
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ctx.parallel_for(temp.shape(), temp.write(), lA.read())->*[] __device__(size_t i, auto temp, auto a) {
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temp(i) = a(i);
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};
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// a++
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ctx.parallel_for(lA.shape(), lA.rw())->*[] __device__(size_t i, auto a) {
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a(i) += 1;
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};
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// tmp *= 2
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ctx.parallel_for(temp.shape(), temp.rw())->*[] __device__(size_t i, auto temp) {
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temp(i) *= 2;
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};
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// a += tmp
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ctx.parallel_for(lA.shape(), temp.read(), lA.rw())->*[] __device__(size_t i, auto temp, auto a) {
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a(i) += temp(i);
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};
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// pop() called automatically at end of iteration
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}
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ctx.finalize();
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return 0;
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}
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