[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/reduce/sum.cu

@@ -0,0 +1,32 @@
+// SPDX-FileCopyrightText: Copyright (c) 2011-2023, NVIDIA CORPORATION. All rights reserved.
+// SPDX-License-Identifier: BSD-3
+
+// This benchmark is intended to cover redux instructions on Ampere+ architectures. It specifically uses
+// cuda::std::plus<> instead of a user-defined operator, which CUB recognizes to select an optimized code path.
+
+// Tuning parameters found for signed integer types apply equally for unsigned integer types
+
+#include <nvbench_helper.cuh>
+
+// %RANGE% TUNE_ITEMS_PER_THREAD ipt 7:24:1
+// %RANGE% TUNE_THREADS_PER_BLOCK tpb 128:1024:32
+// %RANGE% TUNE_ITEMS_PER_VEC_LOAD_POW2 ipv 1:2:1
+
+// __half and __nv_bfloat16 are added for full (non-tuning) runs; CUB has fast paths for them (see #9587).
+#ifdef TUNE_T
+using value_types = nvbench::type_list<TUNE_T>;
+#else
+using value_types =
+  push_back_t<all_types
+#  if _CCCL_HAS_NVFP16() && _CCCL_CTK_AT_LEAST(12, 2)
+              ,
+              __half
+#  endif
+#  if _CCCL_HAS_NVBF16() && _CCCL_CTK_AT_LEAST(12, 2)
+              ,
+              __nv_bfloat16
+#  endif
+              >;
+#endif
+using op_t = ::cuda::std::plus<>;
+#include "base.cuh"