[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/cudax/benchmarks/bench/cuco/hashers.cu

@@ -0,0 +1,131 @@
+// SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
+// SPDX-License-Identifier: SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+#include <thrust/device_vector.h>
+
+#include <cuda/std/cstddef>
+#include <cuda/std/cstdint>
+
+#include <cuda/experimental/__cuco/hash_functions.cuh>
+
+#include <nvbench/nvbench.cuh>
+#include <nvbench/range.cuh>
+
+namespace cudax = cuda::experimental;
+
+// repeat hash computation n times
+static constexpr auto n_repeats = 100;
+
+template <cuda::std::int32_t Words>
+struct large_key
+{
+  constexpr __host__ __device__ large_key(cuda::std::int32_t seed) noexcept
+  {
+    for (cuda::std::int32_t i = 0; i < Words; ++i)
+    {
+      data_[i] = seed;
+    }
+  }
+
+private:
+  cuda::std::int32_t data_[Words];
+};
+
+template <cuda::std::int32_t BlockSize, typename Key, typename Hasher, typename OutputIt>
+__global__ void hash_bench_kernel(Hasher hash, size_t n, OutputIt out, bool materialize_result)
+{
+  size_t const gid         = static_cast<size_t>(BlockSize) * blockIdx.x + threadIdx.x;
+  size_t const loop_stride = static_cast<size_t>(gridDim.x) * BlockSize;
+  size_t idx               = gid;
+  using result_t           = decltype(hash(0));
+
+  result_t agg{};
+
+  while (idx < n)
+  {
+    Key key(idx);
+    for (cuda::std::int32_t i = 0; i < n_repeats; ++i)
+    { // execute hash func n times
+      agg += hash(key);
+    }
+    idx += loop_stride;
+  }
+
+  if (materialize_result)
+  {
+    out[gid] = agg;
+  }
+}
+
+// benchmark evaluating performance of various hash functions
+template <typename HasherTag, typename Key>
+void hash_eval(nvbench::state& state, nvbench::type_list<HasherTag, Key>)
+{
+  using Hash = typename HasherTag::template fn<Key>;
+
+  bool const materialize_result = false;
+  constexpr auto block_size     = 128;
+  auto const num_keys           = state.get_int64("NumInputs");
+  auto const grid_size          = (num_keys + block_size * 16 - 1) / block_size * 16;
+  using result_t                = decltype(std::declval<Hash>()(std::declval<cuda::std::int32_t>()));
+
+  thrust::device_vector<result_t> hash_values((materialize_result) ? num_keys : 1);
+
+  state.add_element_count(num_keys);
+
+  state.exec([&](nvbench::launch& launch) {
+    hash_bench_kernel<block_size, Key>
+      <<<grid_size, block_size, 0, launch.get_stream()>>>(Hash{}, num_keys, hash_values.begin(), materialize_result);
+  });
+}
+
+struct xxhash_32_tag
+{
+  template <typename Key>
+  using fn = cudax::cuco::hash<Key, cudax::cuco::hash_algorithm::xxhash_32>;
+};
+
+struct xxhash_64_tag
+{
+  template <typename Key>
+  using fn = cudax::cuco::hash<Key, cudax::cuco::hash_algorithm::xxhash_64>;
+};
+
+struct murmurhash3_32_tag
+{
+  template <typename Key>
+  using fn = cudax::cuco::hash<Key, cudax::cuco::hash_algorithm::murmurhash3_32>;
+};
+
+#if _CCCL_HAS_INT128()
+
+struct murmurhash3_x86_128_tag
+{
+  template <typename Key>
+  using fn = cudax::cuco::hash<Key, cudax::cuco::hash_algorithm::murmurhash3_x86_128>;
+};
+
+struct murmurhash3_x64_128_tag
+{
+  template <typename Key>
+  using fn = cudax::cuco::hash<Key, cudax::cuco::hash_algorithm::murmurhash3_x64_128>;
+};
+
+#endif // _CCCL_HAS_INT128()
+
+NVBENCH_BENCH_TYPES(
+  hash_eval,
+  NVBENCH_TYPE_AXES(
+    nvbench::type_list<xxhash_32_tag,
+                       xxhash_64_tag,
+                       murmurhash3_32_tag
+#if _CCCL_HAS_INT128()
+                       ,
+                       murmurhash3_x86_128_tag,
+                       murmurhash3_x64_128_tag
+#endif // _CCCL_HAS_INT128()
+                       >,
+    nvbench::type_list<cuda::std::int32_t, large_key<4>, large_key<8>, large_key<16>, large_key<32>>))
+  .set_name("hash_function_eval")
+  .set_type_axes_names({"Hash", "Key"})
+  .add_int64_power_of_two_axis("NumInputs", nvbench::range(18, 26, 4));