[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/tests/headers/test_cuda_cccl.py

@@ -0,0 +1,64 @@
+import pytest
+
+import cuda.cccl as cccl
+
+
+def test_version():
+    v = cccl.__version__
+    assert isinstance(v, str)
+
+
+@pytest.fixture
+def inc_paths():
+    return cccl.get_include_paths()
+
+
+def test_headers_has_cuda(inc_paths):
+    assert hasattr(inc_paths, "cuda")
+
+
+def test_headers_has_cub(inc_paths):
+    assert hasattr(inc_paths, "cub")
+
+
+def test_headers_has_cudacxx(inc_paths):
+    assert hasattr(inc_paths, "libcudacxx")
+
+
+def test_headers_has_thrust(inc_paths):
+    assert hasattr(inc_paths, "thrust")
+
+
+def test_headers_as_tuple(inc_paths):
+    tpl = inc_paths.as_tuple()
+    assert len(tpl) == 4
+
+    thrust_, cub_, cudacxx_, cuda_ = tpl
+    assert cuda_ == inc_paths.cuda
+    assert cub_ == inc_paths.cub
+    assert cudacxx_ == inc_paths.libcudacxx
+    assert thrust_ == inc_paths.thrust
+
+
+def test_cub_version(inc_paths):
+    cub_dir = inc_paths.cub / "cub"
+    cub_version = cub_dir / "version.cuh"
+    assert cub_version.exists()
+
+
+def test_thrust_version(inc_paths):
+    thrust_dir = inc_paths.thrust / "thrust"
+    thrust_version = thrust_dir / "version.h"
+    assert thrust_version.exists()
+
+
+def test_cudacxx_version(inc_paths):
+    cudacxx_dir = inc_paths.libcudacxx / "cuda"
+    cudacxx_version = cudacxx_dir / "version"
+    assert cudacxx_version.exists()
+
+
+def test_nv_target(inc_paths):
+    nv_dir = inc_paths.libcudacxx / "nv"
+    nv_target = nv_dir / "target"
+    assert nv_target.exists()