[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/benchmarks/bench/transform/if.cu

@@ -0,0 +1,38 @@
+// SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+// SPDX-License-Identifier: BSD-3-Clause
+
+#include <thrust/count.h>
+#include <thrust/device_vector.h>
+#include <thrust/transform.h>
+
+#include <nvbench_helper.cuh>
+
+template <typename T>
+static void negate_if(nvbench::state& state, nvbench::type_list<T>)
+{
+  const auto n       = static_cast<std::size_t>(state.get_int64("Elements"));
+  const auto entropy = str_to_entropy(state.get_string("Entropy"));
+  const auto val     = lerp_min_max<T>(entropy_to_probability(entropy));
+  auto transform_op  = ::cuda::std::negate<T>{};
+  auto select_op     = less_then_t<T>{val};
+
+  thrust::device_vector<T> input = generate(n);
+  thrust::device_vector<T> output(n, thrust::no_init);
+
+  const auto selected_elements = thrust::count_if(input.cbegin(), input.cend(), select_op);
+  state.add_element_count(n);
+  state.add_global_memory_reads<T>(n);
+  state.add_global_memory_writes<T>(selected_elements);
+
+  caching_allocator_t alloc; // transform_if shouldn't allocate, but let's be consistent
+  state.exec(
+    nvbench::exec_tag::gpu | nvbench::exec_tag::no_batch | nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+      thrust::transform_if(policy(alloc, launch), input.begin(), input.end(), output.begin(), transform_op, select_op);
+    });
+}
+
+NVBENCH_BENCH_TYPES(negate_if, NVBENCH_TYPE_AXES(integral_types))
+  //  .set_name("negate_if")
+  .set_type_axes_names({"T{ct}"})
+  .add_int64_power_of_two_axis("Elements", nvbench::range(16, 28, 4))
+  .add_string_axis("Entropy", {"1.000", "0.544", "0.000"});