[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/cub/examples/device/CMakeLists.txt

@@ -0,0 +1,18 @@
+file(
+  GLOB_RECURSE example_srcs
+  RELATIVE "${CMAKE_CURRENT_LIST_DIR}"
+  CONFIGURE_DEPENDS
+  example_*.cu
+)
+
+foreach (example_src IN LISTS example_srcs)
+  get_filename_component(example_name "${example_src}" NAME_WE)
+  string(
+    REGEX REPLACE
+    "^example_device_"
+    "device."
+    example_name
+    "${example_name}"
+  )
+  cub_add_example(target_name ${example_name} "${example_src}")
+endforeach()