[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/cuda/wrap_pointer.cu

@@ -0,0 +1,27 @@
+#include <thrust/device_ptr.h>
+#include <thrust/fill.h>
+
+#include <cuda.h>
+
+int main()
+{
+  size_t N = 10;
+
+  // obtain raw pointer to device memory
+  int* raw_ptr;
+  cudaMalloc((void**) &raw_ptr, N * sizeof(int));
+
+  // wrap raw pointer with a device_ptr
+  thrust::device_ptr<int> dev_ptr = thrust::device_pointer_cast(raw_ptr);
+
+  // use device_ptr in Thrust algorithms
+  thrust::fill(dev_ptr, dev_ptr + static_cast<std::ptrdiff_t>(N), (int) 0);
+
+  // access device memory transparently through device_ptr
+  dev_ptr[0] = 1;
+
+  // free memory
+  cudaFree(raw_ptr);
+
+  return 0;
+}