[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/python/cuda_cccl/README.md

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+# CUDA CCCL Python Package
+
+[`cuda.cccl`](https://nvidia.github.io/cccl/python)
+provides a Pythonic interface to the
+[CUDA Core Compute Libraries](https://nvidia.github.io/cccl/cpp.html#cccl-cpp-libraries).
+It provides the following modules:
+
+- **`cuda.compute`** - Device-level parallel algorithms (reduce, scan, sort, etc.) and iterators
+- **`cuda.cccl.headers`** - Programmatic access to CCCL headers
+
+## Installation
+
+Install from PyPI:
+
+```bash
+pip install cuda-cccl[cu13]  # For CUDA 13.x (pip-installed cuda-toolkit)
+pip install cuda-cccl[cu12]  # For CUDA 12.x (pip-installed cuda-toolkit)
+```
+
+If you already have a CUDA toolkit on your system and do not want pip to
+install it, use the `sysctk` variants:
+
+```bash
+pip install cuda-cccl[sysctk13]  # For CUDA 13.x (system CUDA toolkit)
+pip install cuda-cccl[sysctk12]  # For CUDA 12.x (system CUDA toolkit)
+```
+
+For a minimal install without Numba (useful when supplying pre-compiled operators):
+
+```bash
+pip install cuda-cccl[minimal-cu13]      # pip-installed cuda-toolkit
+pip install cuda-cccl[minimal-sysctk13]  # system CUDA toolkit
+```
+
+Install from conda-forge:
+
+```bash
+conda install -c conda-forge cccl-python
+```
+
+**Requirements:** Python 3.10+, CUDA Toolkit 12.x or 13.x, NVIDIA GPU with Compute Capability 7.5+
+
+## Documentation
+
+For complete documentation, examples, and API reference, visit:
+
+- **Full Documentation**: [nvidia.github.io/cccl/python](https://nvidia.github.io/cccl/python)
+- **Repository**: [github.com/NVIDIA/cccl](https://github.com/NVIDIA/cccl)
+- **Examples**: [github.com/NVIDIA/cccl/tree/main/python/cuda_cccl/tests/compute/examples](https://github.com/NVIDIA/cccl/tree/main/python/cuda_cccl/tests/compute/examples)