[CCCL] 瘦身 + 补全: 移除 cudax/python/libcudacxx-tests 冗余文件, 新增 c2h 测试助手 + cmake 构建系统 + 8 个 CUDA thrust examples

变更摘要:
- 删除: cudax/ (783 files, 7.2M) — 实验性组件，竞赛不需要
- 删除: python/ (226 files, 2.0M) — Python 绑定，竞赛不需要
- 删除: libcudacxx/{test,benchmarks,codegen,cmake,share} (4432 files, 31M)
  保留: libcudacxx/include/ (1463 headers, cuda::std 编译依赖)
- 新增: c2h/ (27 files) — CUB Catch2 测试辅助头文件，编译 243 个测试必需
- 新增: cmake/ (29 files) — CCCL 原生 CMake 构建系统
- 新增: thrust/examples/cuda/ (7 files) + cpp_integration/ (1 file)
  async_reduce, custom_temporary_allocation, explicit_cuda_stream,
  global_device_vector, range_view, unwrap_pointer, wrap_pointer, device

结果: cccl_upstream 从 74M→35M (瘦身 53%), 核心内容 100% 保留:
  27/27 tuning headers, 78 benchmarks, 243 tests,
  60 thrust examples, 18 CUB examples, 全部编译头文件

cccl_upstream/python/cuda_cccl/benchmarks/compute/partition/three_way.py

@@ -1,138 +0,0 @@
-# Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. ALL RIGHTS RESERVED.
-#
-# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
-
-"""
-Python benchmark for three_way_partition using cuda.compute.
-
-C++ equivalent: cub/benchmarks/bench/partition/three_way.cu
-
-Notes:
-- The C++ benchmark uses Entropy axis to control data distribution
-- Uses less_then_t<T> predicate operators to divide data into three partitions:
-  - First partition: items < left_border (max/3)
-  - Second partition: items < right_border (max*2/3)
-  - Third partition (unselected): items >= right_border
-- T axis covers fundamental types (C++ fundamental_types minus int128)
-- Migration: Python uses FUNDAMENTAL_TYPES; omits OffsetT axis.
-- OffsetT axis is omitted because the Python API does not expose offset type.
-"""
-
-import sys
-from pathlib import Path
-
-sys.path.insert(0, str(Path(__file__).parent.parent))
-
-import cupy as cp
-import numpy as np
-from utils import FUNDAMENTAL_TYPES, as_cupy_stream, generate_data_with_entropy
-
-import cuda.bench as bench
-from cuda.compute import make_three_way_partition
-
-
-def bench_three_way_partition(state: bench.State):
-    type_str = state.get_string("T{ct}")
-    dtype = FUNDAMENTAL_TYPES[type_str]
-    num_elements = int(state.get_int64("Elements{io}"))
-    entropy_str = state.get_string("Entropy")
-
-    alloc_stream = as_cupy_stream(state.get_stream())
-
-    if np.issubdtype(dtype, np.integer):
-        info = np.iinfo(dtype)
-        min_val = 0
-        max_val = info.max
-    else:
-        info = np.finfo(dtype)
-        min_val = 0.0
-        max_val = info.max
-
-    left_border = max_val // 3 if np.issubdtype(dtype, np.integer) else max_val / 3
-    right_border = left_border * 2
-
-    d_in = generate_data_with_entropy(
-        num_elements,
-        dtype,
-        entropy_str,
-        alloc_stream,
-        min_val=min_val,
-        max_val=max_val,
-    )
-
-    with alloc_stream:
-        d_first_part_out = cp.empty(num_elements, dtype=dtype)
-        d_second_part_out = cp.empty(num_elements, dtype=dtype)
-        d_unselected_out = cp.empty(num_elements, dtype=dtype)
-        # d_num_selected_out stores [num_first_part, num_second_part]
-        d_num_selected_out = cp.empty(2, dtype=np.int32)
-
-    alloc_stream.synchronize()
-
-    # Convert borders to the correct type for closure capture
-    left_thresh = dtype(left_border)
-    right_thresh = dtype(right_border)
-
-    def select_first_part(x):
-        return x < left_thresh
-
-    def select_second_part(x):
-        return x < right_thresh
-
-    partitioner = make_three_way_partition(
-        d_in=d_in,
-        d_first_part_out=d_first_part_out,
-        d_second_part_out=d_second_part_out,
-        d_unselected_out=d_unselected_out,
-        d_num_selected_out=d_num_selected_out,
-        select_first_part_op=select_first_part,
-        select_second_part_op=select_second_part,
-    )
-
-    temp_storage_bytes = partitioner(
-        temp_storage=None,
-        d_in=d_in,
-        d_first_part_out=d_first_part_out,
-        d_second_part_out=d_second_part_out,
-        d_unselected_out=d_unselected_out,
-        d_num_selected_out=d_num_selected_out,
-        select_first_part_op=select_first_part,
-        select_second_part_op=select_second_part,
-        num_items=num_elements,
-    )
-    with alloc_stream:
-        temp_storage = cp.empty(temp_storage_bytes, dtype=np.uint8)
-
-    state.add_element_count(num_elements)
-    state.add_global_memory_reads(num_elements * d_in.dtype.itemsize)
-    state.add_global_memory_writes(num_elements * d_in.dtype.itemsize)
-    # C++ reports add_global_memory_writes<offset_t>(1) — 1 element of offset type.
-    state.add_global_memory_writes(d_num_selected_out.dtype.itemsize)
-
-    def launcher(launch: bench.Launch):
-        partitioner(
-            temp_storage=temp_storage,
-            d_in=d_in,
-            d_first_part_out=d_first_part_out,
-            d_second_part_out=d_second_part_out,
-            d_unselected_out=d_unselected_out,
-            d_num_selected_out=d_num_selected_out,
-            select_first_part_op=select_first_part,
-            select_second_part_op=select_second_part,
-            num_items=num_elements,
-            stream=launch.get_stream(),
-        )
-
-    state.exec(launcher, batched=False)
-
-
-if __name__ == "__main__":
-    b = bench.register(bench_three_way_partition)
-    b.set_name("base")
-
-    b.add_string_axis("T{ct}", list(FUNDAMENTAL_TYPES.keys()))
-    b.add_int64_power_of_two_axis("Elements{io}", range(16, 29, 4))
-    b.add_string_axis("Entropy", ["1.000", "0.544", "0.000"])
-    # Note: OffsetT axis from C++ is not exposed in Python API
-
-    bench.run_all_benchmarks(sys.argv)