[INFRA] Import NVIDIA/CCCL upstream as optimization reference library

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

cccl_upstream/examples/basic/CMakeLists.txt

@@ -0,0 +1,53 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+cmake_minimum_required(VERSION 3.18 FATAL_ERROR)
+
+project(CCCLDemo CUDA)
+
+# This example uses the CMake Package Manager (CPM) to simplify fetching CCCL from GitHub
+# For more information, see https://github.com/cpm-cmake/CPM.cmake
+include(cmake/CPM.cmake)
+
+# We define these as variables so they can be overridden in CI to pull from a PR instead of CCCL `main`
+# In your project, these variables are unnecessary and you can just use the values directly
+set(
+  CCCL_REPOSITORY
+  "https://github.com/NVIDIA/cccl"
+  CACHE STRING
+  "Git repository to fetch CCCL from"
+)
+set(CCCL_TAG "main" CACHE STRING "Git tag/branch to fetch from CCCL repository")
+
+# This will automatically clone CCCL from GitHub and make the exported cmake targets available
+CPMAddPackage(NAME CCCL GIT_REPOSITORY "${CCCL_REPOSITORY}" GIT_TAG ${CCCL_TAG})
+
+# Default to building for the GPU on the current system
+if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
+  set(CMAKE_CUDA_ARCHITECTURES native)
+endif()
+
+# Creates a cmake executable target for the main program
+add_executable(example_project example.cu)
+target_compile_features(example_project PUBLIC cuda_std_17)
+
+# "Links" the CCCL Cmake target to the `example_project` executable. This configures everything needed to use
+# CCCL headers, including setting up include paths, compiler flags, etc.
+target_link_libraries(example_project PRIVATE CCCL::CCCL)
+
+# This is only relevant for internal testing and not needed by end users.
+include(CTest)
+enable_testing()
+add_test(NAME example_project COMMAND example_project)