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
103 lines
3.1 KiB
Plaintext
103 lines
3.1 KiB
Plaintext
// Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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//
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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#include <cuda/buffer>
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#include <cuda/memory_resource>
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#include <cuda/std/array>
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#include <cuda/stream>
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#include <vector>
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#include <cuda_runtime_api.h>
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template <class T>
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[[gnu::noinline]] void keep_for_debugger(const T& value)
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{
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asm volatile("" : : "g"(&value) : "memory");
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}
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[[gnu::noinline]] void inspect_normal(const cuda::device_buffer<int>& values)
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{
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keep_for_debugger(values);
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}
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using device_buffer_alias = cuda::buffer<int, cuda::mr::device_accessible>;
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[[gnu::noinline]] void inspect_alias(const device_buffer_alias& values)
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{
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keep_for_debugger(values);
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}
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[[gnu::noinline]] void inspect_vector(const std::vector<cuda::device_buffer<int>>& values)
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{
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keep_for_debugger(values[0]);
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keep_for_debugger(values[1]);
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}
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template <class Buffer>
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[[gnu::noinline]] void inspect_host_device(const Buffer& values)
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{
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keep_for_debugger(values);
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}
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[[gnu::noinline]] void inspect_empty(const cuda::device_buffer<int>& values)
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{
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keep_for_debugger(values);
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}
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[[gnu::noinline]] void inspect_before_update(const cuda::device_buffer<int>& values)
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{
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keep_for_debugger(values);
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}
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[[gnu::noinline]] void inspect_after_update(const cuda::device_buffer<int>& values)
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{
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keep_for_debugger(values);
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}
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int main()
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{
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constexpr cuda::device_ref device{0};
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cuda::stream stream{device};
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const cuda::std::array normal_host_values{-56, 22, 94, -13, 7, 41, -82, 0, 63, -5};
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const auto normal_values = cuda::make_device_buffer<int>(stream, device, normal_host_values);
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const cuda::std::array alias_host_values{17, -31, 8, 55};
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const device_buffer_alias aliased_values = cuda::make_device_buffer<int>(stream, device, alias_host_values);
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std::vector<cuda::device_buffer<int>> buffer_vector;
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buffer_vector.emplace_back(cuda::make_device_buffer<int>(stream, device, cuda::std::array{-2, 4, 6}));
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buffer_vector.emplace_back(cuda::make_device_buffer<int>(stream, device, cuda::std::array{11, -9, 27}));
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cuda::mr::legacy_managed_memory_resource managed_resource;
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const cuda::std::array host_device_host_values{3, 14, -15, 92};
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const auto host_device_values = cuda::make_buffer<int>(stream, managed_resource, host_device_host_values);
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const auto empty_values = cuda::make_device_buffer<int>(stream, device);
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const cuda::std::array initial_updated_host_values{1, 2, 3, 4};
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auto updated_values = cuda::make_device_buffer<int>(stream, device, initial_updated_host_values);
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stream.sync();
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inspect_normal(normal_values);
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inspect_alias(aliased_values);
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inspect_vector(buffer_vector);
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inspect_host_device(host_device_values);
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inspect_empty(empty_values);
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inspect_before_update(updated_values);
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const cuda::std::array replacement_host_values{-8, 13, 21, -34};
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if (cudaMemcpyAsync(updated_values.data(),
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replacement_host_values.data(),
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replacement_host_values.size() * sizeof(*updated_values.data()),
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cudaMemcpyDefault,
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stream.get())
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!= cudaSuccess)
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{
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return 1;
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}
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stream.sync();
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inspect_after_update(updated_values);
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}
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