[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/cudax/test/stf/reductions/sum.cu

@@ -1,108 +0,0 @@
-//===----------------------------------------------------------------------===//
-//
-// Part of CUDASTF in CUDA C++ Core Libraries,
-// 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) 2022-2024 NVIDIA CORPORATION & AFFILIATES.
-//
-//===----------------------------------------------------------------------===//
-
-/**
- * @file
- * @brief Use the reduction access mode to add variables concurrently on different places
- */
-
-#include <cuda/experimental/__stf/stream/reduction.cuh>
-#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
-
-using namespace cuda::experimental::stf;
-
-using scalar_t = slice_stream_interface<int, 1>;
-
-template <typename T>
-__global__ void set_value(T* addr, T val)
-{
-  *addr = val;
-}
-
-template <typename T>
-__global__ void add(const T* in_addr, T* inout_addr)
-{
-  // printf("ADD: %d += %d : RES %d\n", *inout_addr, *in_addr, *inout_addr + *in_addr);
-
-  *inout_addr += *in_addr;
-}
-
-template <typename T>
-__global__ void add_val(T* inout_addr, T val)
-{
-  *inout_addr += val;
-}
-
-/*
- * Define a SUM reduction operator over a scalar
- */
-class scalar_sum_t : public stream_reduction_operator_untyped
-{
-public:
-  scalar_sum_t()
-      : stream_reduction_operator_untyped() {};
-
-  void stream_redux_op(
-    logical_data_untyped& d,
-    const data_place& /*unused*/,
-    instance_id_t inout_instance_id,
-    const data_place& /*unused*/,
-    instance_id_t in_instance_id,
-    const exec_place& /*unused*/,
-    cudaStream_t s) override
-  {
-    auto& in_instance    = d.instance<typename scalar_t::element_type>(in_instance_id);
-    auto& inout_instance = d.instance<typename scalar_t::element_type>(inout_instance_id);
-    // fprintf(stderr, "REDUX OP d %p inout (node %d id %d addr %p) in (node %d id %d addr %p)\n", d,
-    //        inout_memory_node, inout_instance_id, *inout_instance, in_memory_node, in_instance_id, *in_instance);
-    add<<<1, 1, 0, s>>>(in_instance.data_handle(), inout_instance.data_handle());
-  }
-
-  void stream_init_op(logical_data_untyped& d,
-                      const data_place& /*unused*/,
-                      instance_id_t out_instance_id,
-                      const exec_place& /*unused*/,
-                      cudaStream_t s) override
-  {
-    auto& out_instance = d.instance<typename scalar_t::element_type>(out_instance_id);
-    // fprintf(stderr, "REDUX INIT d %p memory node %d instance id %d => addr %p\n", d, out_memory_node,
-    //        out_instance_id, *out_instance);
-    set_value<<<1, 1, 0, s>>>(out_instance.data_handle(), 0);
-  }
-};
-
-int main()
-{
-  const int N = 4;
-
-  stream_ctx ctx;
-
-  auto var_handle = ctx.logical_data(shape_of<slice<int>>(1));
-  var_handle.set_symbol("var");
-
-  auto redux_op = std::make_shared<scalar_sum_t>();
-
-  // We add i (total = N(N-1)/2 + initial_value)
-  for (int i = 0; i < N; i++)
-  {
-    ctx.task(var_handle.relaxed(redux_op))->*[&](cudaStream_t stream, auto d_var) {
-      add_val<<<1, 1, 0, stream>>>(d_var.data_handle(), i);
-    };
-  }
-
-  // Check result
-  ctx.task(exec_place::host(), var_handle.read())->*[&](cudaStream_t stream, auto h_var) {
-    cuda_safe_call(cudaStreamSynchronize(stream));
-    int expected = (N * (N - 1)) / 2;
-    EXPECT(h_var(0) == expected);
-  };
-
-  ctx.finalize();
-}