[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/sort.cu

@@ -0,0 +1,194 @@
+#include <thrust/device_vector.h>
+#include <thrust/host_vector.h>
+#include <thrust/random.h>
+#include <thrust/sequence.h>
+#include <thrust/sort.h>
+
+#include <cuda/std/utility>
+
+#include <iomanip>
+#include <iostream>
+#include <numeric>
+
+// Helper routines
+
+void initialize(thrust::device_vector<int>& v)
+{
+  thrust::default_random_engine rng(123456);
+  thrust::uniform_int_distribution<int> dist(10, 99);
+  thrust::host_vector<int> host_data(v.size());
+  for (auto& e : host_data)
+  {
+    e = dist(rng);
+  }
+  v = host_data;
+}
+
+void initialize(thrust::device_vector<float>& v)
+{
+  thrust::default_random_engine rng(123456);
+  thrust::uniform_int_distribution<int> dist(2, 19);
+  thrust::host_vector<float> host_data(v.size());
+  for (auto& e : host_data)
+  {
+    e = static_cast<float>(dist(rng)) / 2.0f;
+  }
+  v = host_data;
+}
+
+void initialize(thrust::device_vector<cuda::std::pair<int, int>>& v)
+{
+  thrust::default_random_engine rng(123456);
+  thrust::uniform_int_distribution<int> dist(0, 9);
+  thrust::host_vector<cuda::std::pair<int, int>> host_data(v.size());
+  for (auto& e : host_data)
+  {
+    int a = dist(rng);
+    int b = dist(rng);
+    e     = cuda::std::make_pair(a, b);
+  }
+  v = host_data;
+}
+
+void initialize(thrust::device_vector<int>& v1, thrust::device_vector<int>& v2)
+{
+  thrust::default_random_engine rng(123456);
+  thrust::uniform_int_distribution<int> dist(10, 99);
+  thrust::host_vector<int> host_data(v1.size());
+  for (auto& e : host_data)
+  {
+    e = dist(rng);
+  }
+  v1 = host_data;
+  thrust::sequence(v2.begin(), v2.end(), 0);
+}
+
+void print(const thrust::device_vector<int>& v)
+{
+  for (const auto& value : v)
+  {
+    std::cout << " " << value;
+  }
+  std::cout << "\n";
+}
+
+void print(const thrust::device_vector<float>& v)
+{
+  for (const auto& value : v)
+  {
+    std::cout << " " << std::fixed << std::setprecision(1) << value;
+  }
+  std::cout << "\n";
+}
+
+void print(const thrust::device_vector<cuda::std::pair<int, int>>& v)
+{
+  for (const auto& p : v)
+  {
+    cuda::std::pair<int, int> local_p = p;
+    std::cout << " (" << local_p.first << "," << local_p.second << ")";
+  }
+  std::cout << "\n";
+}
+
+void print(thrust::device_vector<int>& v1, thrust::device_vector<int> v2)
+{
+  for (size_t i = 0; i < v1.size(); i++)
+  {
+    std::cout << " (" << v1[i] << "," << std::setw(2) << v2[i] << ")";
+  }
+  std::cout << "\n";
+}
+
+// user-defined comparison operator that acts like less<int>,
+// except even numbers are considered to be smaller than odd numbers
+struct evens_before_odds
+{
+  __host__ __device__ bool operator()(int x, int y)
+  {
+    if (x % 2 == y % 2)
+    {
+      return x < y;
+    }
+    else if (x % 2)
+    {
+      return false;
+    }
+    else
+    {
+      return true;
+    }
+  }
+};
+
+int main()
+{
+  size_t N = 16;
+
+  std::cout << "sorting integers\n";
+  {
+    thrust::device_vector<int> keys(N);
+    initialize(keys);
+    print(keys);
+    thrust::sort(keys.begin(), keys.end());
+    print(keys);
+  }
+
+  std::cout << "\nsorting integers (descending)\n";
+  {
+    thrust::device_vector<int> keys(N);
+    initialize(keys);
+    print(keys);
+    thrust::sort(keys.begin(), keys.end(), cuda::std::greater<int>());
+    print(keys);
+  }
+
+  std::cout << "\nsorting integers (user-defined comparison)\n";
+  {
+    thrust::device_vector<int> keys(N);
+    initialize(keys);
+    print(keys);
+    thrust::sort(keys.begin(), keys.end(), evens_before_odds());
+    print(keys);
+  }
+
+  std::cout << "\nsorting floats\n";
+  {
+    thrust::device_vector<float> keys(N);
+    initialize(keys);
+    print(keys);
+    thrust::sort(keys.begin(), keys.end());
+    print(keys);
+  }
+
+  std::cout << "\nsorting pairs\n";
+  {
+    thrust::device_vector<cuda::std::pair<int, int>> keys(N);
+    initialize(keys);
+    print(keys);
+    thrust::sort(keys.begin(), keys.end());
+    print(keys);
+  }
+
+  std::cout << "\nkey-value sorting\n";
+  {
+    thrust::device_vector<int> keys(N);
+    thrust::device_vector<int> values(N);
+    initialize(keys, values);
+    print(keys, values);
+    thrust::sort_by_key(keys.begin(), keys.end(), values.begin());
+    print(keys, values);
+  }
+
+  std::cout << "\nkey-value sorting (descending)\n";
+  {
+    thrust::device_vector<int> keys(N);
+    thrust::device_vector<int> values(N);
+    initialize(keys, values);
+    print(keys, values);
+    thrust::sort_by_key(keys.begin(), keys.end(), values.begin(), cuda::std::greater<int>());
+    print(keys, values);
+  }
+
+  return 0;
+}