[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/include/cuda/__execution/tune.h

@@ -0,0 +1,81 @@
+//===----------------------------------------------------------------------===//
+//
+// 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) 2025 NVIDIA CORPORATION & AFFILIATES.
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef __CUDA___EXECUTION_TUNE_H
+#define __CUDA___EXECUTION_TUNE_H
+
+#include <cuda/std/detail/__config>
+
+#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
+#  pragma GCC system_header
+#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
+#  pragma clang system_header
+#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
+#  pragma system_header
+#endif // no system header
+
+#include <cuda/__device/compute_capability.h>
+#include <cuda/std/__concepts/concept_macros.h>
+#include <cuda/std/__concepts/semiregular.h>
+#include <cuda/std/__execution/env.h>
+#include <cuda/std/__functional/invoke.h>
+#include <cuda/std/__type_traits/is_empty.h>
+
+#include <cuda/std/__cccl/prologue.h>
+
+_CCCL_BEGIN_NAMESPACE_CUDA_EXECUTION
+
+struct __get_tuning_t
+{
+  _CCCL_EXEC_CHECK_DISABLE
+  _CCCL_TEMPLATE(class _Env)
+  _CCCL_REQUIRES(::cuda::std::execution::__queryable_with<_Env, __get_tuning_t>)
+  [[nodiscard]] _CCCL_NODEBUG_API constexpr auto operator()(const _Env& __env) const noexcept
+  {
+    static_assert(noexcept(__env.query(*this)));
+    return __env.query(*this);
+  }
+
+  [[nodiscard]]
+  _CCCL_NODEBUG_API static constexpr auto query(::cuda::std::execution::forwarding_query_t) noexcept -> bool
+  {
+    return true;
+  }
+};
+
+_CCCL_GLOBAL_CONSTANT auto __get_tuning = __get_tuning_t{};
+
+//! @rst
+//! Creates an environment from a pack of policy selectors that can be passed to device-wide parallel algorithms to
+//! select tunings for different target architectures. See the :ref:`policy selector documentation
+//! <cub-policy-selectors>` for more information on how algorithms can be tuned.
+//! @endrst
+template <class... _PolicySelectors>
+[[nodiscard]] _CCCL_NODEBUG_API auto tune(_PolicySelectors...)
+{
+  static_assert((::cuda::std::is_empty_v<_PolicySelectors> && ...), "Policy selectors must be stateless");
+  static_assert((::cuda::std::semiregular<_PolicySelectors> && ...), "Policy selectors must be semiregular types");
+  static_assert((::cuda::std::is_invocable_v<_PolicySelectors, ::cuda::compute_capability> && ...),
+                "Policy selectors must be invocable with cuda::compute_capability");
+
+  // since all the tunings are stateless, let's ignore incoming parameters
+
+  // we use the return type of the policy_selector as tag
+  using tuning_env = ::cuda::std::execution::env<
+    ::cuda::std::execution::prop<decltype(_PolicySelectors{}(::cuda::compute_capability{})), _PolicySelectors>...>;
+
+  return ::cuda::std::execution::prop{__get_tuning_t{}, tuning_env{}};
+}
+
+_CCCL_END_NAMESPACE_CUDA_EXECUTION
+
+#include <cuda/std/__cccl/epilogue.h>
+
+#endif // __CUDA___EXECUTION_TUNE_H