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
2.7 KiB
CMake
76 lines
2.7 KiB
CMake
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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cmake_minimum_required(VERSION 3.18 FATAL_ERROR)
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project(CUDAX_SAMPLES CUDA CXX)
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# This example uses the CMake Package Manager (CPM) to simplify fetching CCCL from GitHub
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# For more information, see https://github.com/cpm-cmake/CPM.cmake
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include(cmake/CPM.cmake)
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# We define these as variables so they can be overridden in CI to pull from a PR instead of CCCL `main`
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# In your project, these variables are unnecessary and you can just use the values directly
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set(
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CCCL_REPOSITORY
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"https://github.com/NVIDIA/cccl"
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CACHE STRING
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"Git repository to fetch CCCL from"
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)
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set(CCCL_TAG "main" CACHE STRING "Git tag/branch to fetch from CCCL repository")
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# This will automatically clone CCCL from GitHub and make the exported cmake targets available
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CPMAddPackage(
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NAME CCCL
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GIT_REPOSITORY "${CCCL_REPOSITORY}"
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GIT_TAG ${CCCL_TAG}
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GIT_SHALLOW ON
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# The following is required to make the `CCCL::cudax` target available:
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OPTIONS "CCCL_ENABLE_UNSTABLE ON"
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)
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# Default to building for the GPU on the current system
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if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
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set(CMAKE_CUDA_ARCHITECTURES native)
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endif()
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add_library(cudax_samples_interface INTERFACE)
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target_link_libraries(cudax_samples_interface INTERFACE CCCL::CCCL CCCL::cudax)
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if ("MSVC" STREQUAL "${CMAKE_CXX_COMPILER_ID}")
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# mdspan on windows only works in C++20 mode
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target_compile_features(cudax_samples_interface INTERFACE cxx_std_20)
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if (MSVC_TOOLSET_VERSION LESS 143)
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# winbase.h(9572): warning C5105: macro expansion producing 'defined' has undefined behavior
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target_compile_options(
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cudax_samples_interface
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INTERFACE
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$<$<COMPILE_LANGUAGE:CXX>:/wd5105>
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$<$<COMPILE_LANG_AND_ID:CUDA,NVIDIA>:-Xcompiler=/wd5105>
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)
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endif()
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endif()
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# The vector_add sample demonstrates a simple CUDA kernel that adds two vectors
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add_executable(vector_add vector_add/vector_add.cu)
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target_link_libraries(vector_add PUBLIC cudax_samples_interface)
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# This is only relevant for internal testing and not needed by end users.
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include(CTest)
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enable_testing()
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add_test(NAME vector_add COMMAND vector_add)
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