[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/histogram/even.py

@@ -1,132 +0,0 @@
-# Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. ALL RIGHTS RESERVED.
-#
-# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
-
-"""
-Python benchmark for histogram_even using cuda.compute.
-
-C++ equivalent: cub/benchmarks/bench/histogram/even.cu
-
-Notes:
-- The C++ benchmark uses Entropy axis with nvbench_helper bit entropy generation
-- Migration: Python matches the bitwise-AND entropy approach and skips some I8/I16 large-bin cases due to CUDA errors.
-"""
-
-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 TYPE_MAP
-from utils import as_cupy_stream, generate_data_with_entropy
-
-import cuda.bench as bench
-from cuda.compute import make_histogram_even
-
-
-def get_upper_level(dtype, num_bins, num_elements):
-    """
-    Compute upper level for histogram bins.
-    Mirrors C++ get_upper_level() from histogram_common.cuh
-    """
-    if np.issubdtype(dtype, np.integer):
-        # For integer types, upper_level = min(num_bins, max_value_for_type)
-        max_val = np.iinfo(dtype).max
-        return dtype(min(num_bins, max_val))
-    else:
-        # For floating point types, upper_level = num_elements
-        return dtype(num_elements)
-
-
-def bench_histogram_even(state: bench.State):
-    type_str = state.get_string("SampleT{ct}")
-    dtype = TYPE_MAP[type_str]
-    num_elements = int(state.get_int64("Elements{io}"))
-    num_bins = int(state.get_int64("Bins"))
-    entropy_str = state.get_string("Entropy")
-
-    # Skip invalid configurations (like C++ does)
-    # For integer types, skip if num_bins > max value representable by SampleT
-    if np.issubdtype(dtype, np.integer):
-        max_val = np.iinfo(dtype).max
-        if num_bins > max_val:
-            state.skip("Number of bins exceeds what SampleT can represent")
-            return
-
-    num_levels = num_bins + 1
-    lower_level = dtype(0)
-    upper_level = get_upper_level(dtype, num_bins, num_elements)
-
-    alloc_stream = as_cupy_stream(state.get_stream())
-
-    d_samples = generate_data_with_entropy(
-        num_elements,
-        dtype,
-        entropy_str,
-        alloc_stream,
-        min_val=lower_level,
-        max_val=upper_level,
-    )
-
-    # Output histogram (counter type is int32 in C++)
-    with alloc_stream:
-        d_histogram = cp.zeros(num_bins, dtype=np.int32)
-
-    alloc_stream.synchronize()
-
-    h_num_output_levels = np.array([num_levels], dtype=np.int32)
-    h_lower_level = np.array([lower_level], dtype=dtype)
-    h_upper_level = np.array([upper_level], dtype=dtype)
-
-    histogrammer = make_histogram_even(
-        d_samples=d_samples,
-        d_histogram=d_histogram,
-        h_num_output_levels=h_num_output_levels,
-        h_lower_level=h_lower_level,
-        h_upper_level=h_upper_level,
-        num_samples=num_elements,
-    )
-
-    temp_storage_bytes = histogrammer(
-        temp_storage=None,
-        d_samples=d_samples,
-        d_histogram=d_histogram,
-        h_num_output_levels=h_num_output_levels,
-        h_lower_level=h_lower_level,
-        h_upper_level=h_upper_level,
-        num_samples=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_samples.dtype.itemsize)
-    state.add_global_memory_writes(num_bins * d_histogram.dtype.itemsize)
-
-    def launcher(launch: bench.Launch):
-        histogrammer(
-            temp_storage=temp_storage,
-            d_samples=d_samples,
-            d_histogram=d_histogram,
-            h_num_output_levels=h_num_output_levels,
-            h_lower_level=h_lower_level,
-            h_upper_level=h_upper_level,
-            num_samples=num_elements,
-            stream=launch.get_stream(),
-        )
-
-    state.exec(launcher, batched=False)
-
-
-if __name__ == "__main__":
-    b = bench.register(bench_histogram_even)
-    b.set_name("base")
-
-    b.add_string_axis("SampleT{ct}", list(TYPE_MAP.keys()))
-    b.add_int64_power_of_two_axis("Elements{io}", range(16, 29, 4))
-    b.add_int64_axis("Bins", [32, 128, 2048, 2097152])
-    b.add_string_axis("Entropy", ["0.201", "1.000"])
-
-    bench.run_all_benchmarks(sys.argv)