[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/libcudacxx/benchmarks/bench/is_heap/basic.cu

@@ -0,0 +1,85 @@
+//===----------------------------------------------------------------------===//
+//
+// Part of libcu++, the C++ Standard Library for your entire system,
+// under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+// SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES.
+//
+//===----------------------------------------------------------------------===//
+
+#include <thrust/device_vector.h>
+#include <thrust/fill.h>
+
+#include <cuda/functional>
+#include <cuda/std/execution>
+#include <cuda/stream>
+
+#include "nvbench_helper.cuh"
+
+// All-zero is a valid heap; setting one element to 1 forces a violation at
+// that child index since its parent is still 0.
+template <typename T>
+static void prepare_input(thrust::device_vector<T>& d, std::size_t violation_point)
+{
+  thrust::fill(d.begin(), d.end(), T{0});
+  if (violation_point >= 1 && violation_point < d.size())
+  {
+    d[violation_point] = T{1};
+  }
+}
+
+template <typename T>
+static void basic(nvbench::state& state, nvbench::type_list<T>)
+{
+  const auto elements        = static_cast<std::size_t>(state.get_int64("Elements"));
+  const auto violation_frac  = state.get_float64("ViolationAt");
+  const auto violation_point = cuda::std::clamp<std::size_t>(
+    static_cast<std::size_t>(static_cast<double>(elements) * violation_frac), std::size_t{0}, elements - 1);
+
+  thrust::device_vector<T> dinput(elements, thrust::no_init);
+  prepare_input(dinput, violation_point);
+
+  state.add_global_memory_reads<T>(2 * violation_point);
+  state.add_global_memory_writes<size_t>(1);
+
+  caching_allocator_t alloc{};
+
+  state.exec(nvbench::exec_tag::gpu | nvbench::exec_tag::no_batch | nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch) {
+               do_not_optimize(cuda::std::is_heap(cuda_policy(alloc, launch), dinput.begin(), dinput.end()));
+             });
+}
+
+NVBENCH_BENCH_TYPES(basic, NVBENCH_TYPE_AXES(fundamental_types))
+  .set_name("base")
+  .add_int64_power_of_two_axis("Elements", nvbench::range(16, 28, 4))
+  .add_float64_axis("ViolationAt", std::vector{1.0, 0.5, 0.01});
+
+template <typename T>
+static void with_predicate(nvbench::state& state, nvbench::type_list<T>)
+{
+  const auto elements        = static_cast<std::size_t>(state.get_int64("Elements"));
+  const auto violation_frac  = state.get_float64("ViolationAt");
+  const auto violation_point = cuda::std::clamp<std::size_t>(
+    static_cast<std::size_t>(static_cast<double>(elements) * violation_frac), std::size_t{0}, elements - 1);
+
+  thrust::device_vector<T> dinput(elements, thrust::no_init);
+  prepare_input(dinput, violation_point);
+
+  state.add_global_memory_reads<T>(2 * violation_point);
+  state.add_global_memory_writes<size_t>(1);
+
+  caching_allocator_t alloc{};
+
+  state.exec(nvbench::exec_tag::gpu | nvbench::exec_tag::no_batch | nvbench::exec_tag::sync,
+             [&](nvbench::launch& launch) {
+               do_not_optimize(
+                 cuda::std::is_heap(cuda_policy(alloc, launch), dinput.begin(), dinput.end(), cuda::std::less<>{}));
+             });
+}
+
+NVBENCH_BENCH_TYPES(with_predicate, NVBENCH_TYPE_AXES(fundamental_types))
+  .set_name("with_predicate")
+  .add_int64_power_of_two_axis("Elements", nvbench::range(16, 28, 4))
+  .add_float64_axis("ViolationAt", std::vector{1.0, 0.5, 0.01});