[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/permutation_iterator.cu

@@ -0,0 +1,27 @@
+#include <thrust/device_vector.h>
+#include <thrust/iterator/permutation_iterator.h>
+#include <thrust/reduce.h>
+
+#include <iostream>
+
+// this example fuses a gather operation with a reduction for
+// greater efficiency than separate gather() and reduce() calls
+
+int main()
+{
+  // gather locations
+  thrust::device_vector<int> map = {3, 1, 0, 5};
+
+  // array to gather from
+  thrust::device_vector<int> source = {10, 20, 30, 40, 50, 60};
+
+  // fuse gather with reduction:
+  //   sum = source[map[0]] + source[map[1]] + ...
+  int sum = thrust::reduce(thrust::make_permutation_iterator(source.begin(), map.begin()),
+                           thrust::make_permutation_iterator(source.begin(), map.end()));
+
+  // print sum
+  std::cout << "sum is " << sum << '\n';
+
+  return 0;
+}