[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/thrust/examples/cpp_integration/README

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+This example shows how to link a Thrust program contained in
+a .cu file with a C++ program contained in a .cpp file.  Note
+that device_vector only appears in the .cu file while host_vector
+appears in both.  This relects the fact that algorithms on device
+vectors are only available when the contents of the program are
+located in a .cu file and compiled with the nvcc compiler.
+
+On a Linux system where Thrust is installed in the default location
+we can use the following procedure to compile the two parts of the
+program and link them together.
+
+  $ nvcc -O2 -c device.cu
+  $ g++  -O2 -c host.cpp   -I/usr/local/cuda/include/
+  $ nvcc -o tester device.o host.o
+
+Alternatively, we can use g++ to perform final linking step.
+
+  $ nvcc -O2 -c device.cu
+  $ g++  -O2 -c host.cpp   -I/usr/local/cuda/include/
+  $ g++ -o tester device.o host.o -L/usr/local/cuda/lib64 -lcudart