[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/freeze/task_fence.cu

@@ -1,98 +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 Freeze data in read-only fashion
- *
- */
-
-#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
-#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
-
-using namespace cuda::experimental::stf;
-
-int X0(int i)
-{
-  return 17 * i + 45;
-}
-
-__global__ void print(slice<int> s)
-{
-  int tid      = blockIdx.x * blockDim.x + threadIdx.x;
-  int nthreads = gridDim.x * blockDim.x;
-
-  for (int i = tid; i < s.size(); i += nthreads)
-  {
-    printf("%d %d\n", i, s(i));
-  }
-}
-
-int main()
-{
-  stream_ctx ctx;
-
-  const int N = 16;
-  int X[N];
-
-  for (int i = 0; i < N; i++)
-  {
-    X[i] = X0(i);
-  }
-
-  auto lX = ctx.logical_data(X).set_symbol("X");
-  auto lY = ctx.logical_data(lX.shape()).set_symbol("Y");
-
-  ctx.parallel_for(lX.shape(), lX.rw()).set_symbol("X=2X")->*[] __device__(size_t i, auto x) {
-    x(i) *= 2;
-  };
-
-  // test 1 : implicit sync of gets
-  {
-    auto fx      = ctx.freeze(lX);
-    auto [dX, _] = fx.get(data_place::current_device());
-
-    // the stream returned by fence should depend on the get operation
-    auto stream2 = ctx.fence();
-    print<<<8, 4, 0, stream2>>>(dX);
-
-    ctx.parallel_for(lX.shape(), lX.read(), lY.write()).set_symbol("Y=X")->*[] __device__(size_t i, auto x, auto y) {
-      y(i) = x(i);
-    };
-
-    fx.unfreeze(stream2);
-  }
-
-  // test 2 : unfreeze with no events due to user sync
-  {
-    auto fx      = ctx.freeze(lX);
-    auto [dX, _] = fx.get(data_place::current_device());
-
-    // the stream returned by fence should depend on the get operation
-    auto stream2 = ctx.fence();
-    print<<<8, 4, 0, stream2>>>(dX);
-
-    // We synchronize so there is nothing to depend on anymore
-    cudaStreamSynchronize(stream2);
-    fx.unfreeze(event_list());
-  }
-
-  ctx.parallel_for(lX.shape(), lX.rw()).set_symbol("X+=1")->*[] __device__(size_t i, auto x) {
-    x(i) += 1;
-  };
-
-  ctx.finalize();
-
-  for (int i = 0; i < N; i++)
-  {
-    EXPECT(X[i] == 2 * X0(i) + 1);
-  }
-}