[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/cub/benchmarks/bench/scan/exclusive/deterministic.cu

@@ -0,0 +1,57 @@
+// SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+#include <cub/device/device_scan.cuh>
+
+#include <cuda/__execution/determinism.h>
+#include <cuda/__execution/require.h>
+#include <cuda/std/__functional/invoke.h>
+
+#include <nvbench_helper.cuh>
+
+template <typename T, typename OffsetT>
+static void exclusive_scan(nvbench::state& state, nvbench::type_list<T, OffsetT>)
+try
+{
+  using init_value_t = T;
+  using offset_t     = OffsetT;
+  using scan_op_t    = ::cuda::std::plus<T>;
+
+  const auto elements = static_cast<std::size_t>(state.get_int64("Elements{io}"));
+
+  thrust::device_vector<T> input = generate(elements);
+  thrust::device_vector<T> output(elements, thrust::no_init);
+
+  const T* d_input = thrust::raw_pointer_cast(input.data());
+  T* d_output      = thrust::raw_pointer_cast(output.data());
+
+  state.add_element_count(elements);
+  state.add_global_memory_reads<T>(elements, "Size");
+  state.add_global_memory_writes<T>(elements);
+
+  caching_allocator_t alloc;
+  state.exec(nvbench::exec_tag::gpu | nvbench::exec_tag::no_batch, [&](nvbench::launch& launch) {
+    auto env = cub_bench_env(alloc, launch, cuda::execution::require(cuda::execution::determinism::run_to_run));
+    _CCCL_TRY_CUDA_API(
+      cub::DeviceScan::ExclusiveScan,
+      "ExclusiveScan failed",
+      d_input,
+      d_output,
+      scan_op_t{},
+      init_value_t{},
+      static_cast<offset_t>(elements),
+      env);
+  });
+}
+catch (const std::bad_alloc&)
+{
+  state.skip("Skipping: out of memory.");
+}
+
+using types   = nvbench::type_list<float, double>;
+using offsets = nvbench::type_list<int64_t>;
+
+NVBENCH_BENCH_TYPES(exclusive_scan, NVBENCH_TYPE_AXES(types, offsets))
+  .set_name("base")
+  .set_type_axes_names({"T{ct}", "OffsetT{ct}"})
+  .add_int64_power_of_two_axis("Elements{io}", nvbench::range(16, 28, 4));