[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/host.cpp

@@ -0,0 +1,27 @@
+#include <thrust/generate.h>
+#include <thrust/host_vector.h>
+#include <thrust/random.h>
+#include <thrust/sort.h>
+
+#include <cstdlib>
+#include <iostream>
+#include <iterator>
+
+// defines the function prototype
+#include "device.h"
+
+int main()
+{
+  // generate 20 random numbers on the host
+  thrust::host_vector<int> h_vec(20);
+  thrust::default_random_engine rng;
+  thrust::generate(h_vec.begin(), h_vec.end(), rng);
+
+  // interface to CUDA code
+  sort_on_device(h_vec);
+
+  // print sorted array
+  thrust::copy(h_vec.begin(), h_vec.end(), std::ostream_iterator<int>(std::cout, "\n"));
+
+  return 0;
+}