[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/summed_area_table.cu

@@ -0,0 +1,119 @@
+#include <thrust/device_vector.h>
+#include <thrust/functional.h>
+#include <thrust/gather.h>
+#include <thrust/host_vector.h>
+#include <thrust/iterator/counting_iterator.h>
+#include <thrust/iterator/transform_iterator.h>
+#include <thrust/scan.h>
+
+#include <iomanip>
+#include <iostream>
+
+// This example computes a summed area table using segmented scan
+// http://en.wikipedia.org/wiki/Summed_area_table
+
+// convert a linear index to a linear index in the transpose
+struct transpose_index
+{
+  size_t m, n;
+
+  __host__ __device__ transpose_index(size_t _m, size_t _n)
+      : m(_m)
+      , n(_n)
+  {}
+
+  __host__ __device__ size_t operator()(size_t linear_index)
+  {
+    size_t i = linear_index / n;
+    size_t j = linear_index % n;
+
+    return m * j + i;
+  }
+};
+
+// convert a linear index to a row index
+struct row_index
+{
+  size_t n;
+
+  __host__ __device__ row_index(size_t _n)
+      : n(_n)
+  {}
+
+  __host__ __device__ size_t operator()(size_t i)
+  {
+    return i / n;
+  }
+};
+
+// transpose an M-by-N array
+template <typename T>
+void transpose(size_t m, size_t n, thrust::device_vector<T>& src, thrust::device_vector<T>& dst)
+{
+  thrust::counting_iterator<size_t> indices(0);
+
+  thrust::gather(thrust::make_transform_iterator(indices, transpose_index(n, m)),
+                 thrust::make_transform_iterator(indices, transpose_index(n, m)) + dst.size(),
+                 src.begin(),
+                 dst.begin());
+}
+
+// scan the rows of an M-by-N array
+template <typename T>
+void scan_horizontally(size_t n, thrust::device_vector<T>& d_data)
+{
+  thrust::counting_iterator<size_t> indices(0);
+
+  thrust::inclusive_scan_by_key(
+    thrust::make_transform_iterator(indices, row_index(n)),
+    thrust::make_transform_iterator(indices, row_index(n)) + d_data.size(),
+    d_data.begin(),
+    d_data.begin());
+}
+
+// print an M-by-N array
+template <typename T>
+void print(size_t m, size_t n, thrust::device_vector<T>& d_data)
+{
+  thrust::host_vector<T> h_data = d_data;
+
+  for (size_t i = 0; i < m; i++)
+  {
+    for (size_t j = 0; j < n; j++)
+    {
+      std::cout << std::setw(8) << h_data[i * n + j] << " ";
+    }
+    std::cout << "\n";
+  }
+}
+
+int main()
+{
+  size_t m = 3; // number of rows
+  size_t n = 4; // number of columns
+
+  // 2d array stored in row-major order [(0,0), (0,1), (0,2) ... ]
+  thrust::device_vector<int> data(m * n, 1);
+
+  std::cout << "[step 0] initial array" << '\n';
+  print(m, n, data);
+
+  std::cout << "[step 1] scan horizontally" << '\n';
+  scan_horizontally(n, data);
+  print(m, n, data);
+
+  std::cout << "[step 2] transpose array" << '\n';
+  thrust::device_vector<int> temp(m * n);
+  transpose(m, n, data, temp);
+  print(n, m, temp);
+
+  std::cout << "[step 3] scan transpose horizontally" << '\n';
+  scan_horizontally(m, temp);
+  print(n, m, temp);
+
+  std::cout << "[step 4] transpose the transpose" << '\n';
+  transpose(n, m, temp, data);
+  print(m, n, data);
+
+  return 0;
+}