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
89 lines
2.1 KiB
Plaintext
89 lines
2.1 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 Experiment with local context nesting
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*
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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 sctx;
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int array[1024];
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for (size_t i = 0; i < 1024; i++)
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{
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array[i] = 1 + i * i;
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}
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auto lC = sctx.logical_data(array);
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auto lA = sctx.logical_data(lC.shape());
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lA.set_symbol("A");
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auto lA2 = sctx.logical_data(shape_of<slice<int>>(1024));
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lA2.set_symbol("A2");
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sctx.parallel_for(lA.shape(), lA.write())->*[] __device__(size_t i, auto a) {
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a(i) = 42 + 2 * i;
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};
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/* Create nested graph */
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{
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stackable_ctx::graph_scope_guard scope{sctx};
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auto lB = sctx.logical_data(shape_of<slice<int>>(512));
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lB.set_symbol("B");
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sctx.parallel_for(lB.shape(), lB.write())->*[] __device__(size_t i, auto b) {
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b(i) = 17 - 3 * i;
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};
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sctx.parallel_for(lA2.shape(), lA2.write())->*[] __device__(size_t i, auto a2) {
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a2(i) = 5 * i + 4;
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};
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sctx.parallel_for(lB.shape(), lA.read(), lB.rw())->*[] __device__(size_t i, auto a, auto b) {
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b(i) += a(i);
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};
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sctx.parallel_for(lB.shape(), lB.read(), lC.rw())->*[] __device__(size_t i, auto b, auto c) {
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c(i) += b(i);
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};
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}
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sctx.host_launch(lA2.read())->*[](auto a2) {
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for (size_t i = 0; i < a2.size(); i++)
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{
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EXPECT(a2(i) == 5 * i + 4);
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}
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};
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// Do the same check in another graph
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{
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stackable_ctx::graph_scope_guard scope{sctx};
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lA2.push(access_mode::read);
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sctx.host_launch(lA2.read())->*[](auto a2) {
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for (size_t i = 0; i < a2.size(); i++)
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{
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EXPECT(a2(i) == 5 * i + 4);
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}
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};
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}
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sctx.finalize();
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}
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