[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/README.md

@@ -0,0 +1,12 @@
+
+This directory contains examples of how to use CCCL in your project.
+
+See the `README.md` in each subdirectory for more information.
+
+To build and run only the examples, run the following commands from the root directory of the repository:
+
+```bash
+cmake -S . -B build -DCCCL_ENABLE_EXAMPLES=ON -DCCCL_ENABLE_THRUST=OFF -DCCCL_ENABLE_CUB=OFF -DCCCL_ENABLE_LIBCUDACXX=OFF -DCCCL_ENABLE_TESTING=OFF
+cmake --build build
+ctest --test-dir build --output-on-failure
+```