[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/examples/stf/jacobi.cu

@@ -1,153 +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 Jacobi method with launch
- *
- */
-
-#include <cuda/experimental/stf.cuh>
-
-#include <iostream>
-
-using namespace cuda::experimental::stf;
-
-/* Implement atomicMax with a compare and swap */
-_CCCL_DEVICE double atomicMax(double* address, double val)
-{
-  unsigned long long int* address_as_ull = (unsigned long long int*) address;
-  unsigned long long int old             = *address_as_ull, assumed;
-
-  do
-  {
-    assumed = old;
-    old     = atomicCAS(address_as_ull, assumed, __double_as_longlong(fmax(val, __longlong_as_double(assumed))));
-
-    // Note: uses integer comparison to avoid hang in case of NaN (since NaN != NaN)
-  } while (assumed != old);
-
-  return __longlong_as_double(old);
-}
-
-template <typename thread_hierarchy_t>
-_CCCL_DEVICE double reduce_max(thread_hierarchy_t& t, double local_max)
-{
-  auto ti             = t.inner();
-  slice<double> error = t.template storage<double>(0);
-
-  error(0) = 0.0;
-  t.sync();
-
-  // Note we do not use t.static_width(1) because t is a runtime variable so it
-  // cannot be used directly to statically evaluate the size.
-  __shared__ double block_max[thread_hierarchy_t::static_width(1)];
-  block_max[ti.rank()] = local_max;
-  for (size_t s = ti.size() / 2; s > 0; s /= 2)
-  {
-    if (ti.rank() < s)
-    {
-      block_max[ti.rank()] = fmax(block_max[ti.rank() + s], block_max[ti.rank()]);
-    }
-    ti.sync();
-  }
-
-  if (ti.rank() == 0)
-  {
-    atomicMax(&error(0), block_max[0]);
-  }
-  t.sync();
-
-  return error(0);
-}
-
-int main(int argc, char** argv)
-{
-  context ctx;
-
-  size_t n        = 4096;
-  size_t m        = 4096;
-  size_t iter_max = 100;
-  double tol      = 0.0000001;
-
-  if (argc > 2)
-  {
-    n = atol(argv[1]);
-    m = atol(argv[2]);
-  }
-
-  if (argc > 3)
-  {
-    iter_max = atoi(argv[3]);
-  }
-
-  if (argc > 4)
-  {
-    tol = atof(argv[4]);
-  }
-
-  auto lA    = ctx.logical_data(shape_of<slice<double, 2>>(m, n));
-  auto lAnew = ctx.logical_data(lA.shape());
-
-  auto all_devs = exec_place::all_devices();
-
-  ctx.parallel_for(blocked_partition(), all_devs, lA.shape(), lA.write(), lAnew.write()).set_symbol("init")->*
-    [=] _CCCL_DEVICE(size_t i, size_t j, auto A, auto Anew) {
-      A(i, j) = (i == j) ? 10.0 : -1.0;
-    };
-
-  cudaEvent_t start, stop;
-
-  cuda_safe_call(cudaEventCreate(&start));
-  cuda_safe_call(cudaEventCreate(&stop));
-
-  cuda_safe_call(cudaEventRecord(start, ctx.fence()));
-
-  auto spec = con(con<64>(), mem(sizeof(double)));
-
-  ctx.launch(spec, all_devs, lA.rw(), lAnew.write())->*[iter_max, tol, n, m] _CCCL_DEVICE(auto t, auto A, auto Anew) {
-    auto ti = t.inner();
-    for (size_t iter = 0; iter < iter_max; iter++)
-    {
-      // thread-local maximum error
-      double local_error = 0.0;
-
-      for (auto [i, j] : t.apply_partition(inner<1>(shape(A))))
-      {
-        Anew(i, j) = 0.25 * (A(i - 1, j) + A(i + 1, j) + A(i, j - 1) + A(i, j + 1));
-
-        local_error = fmax(local_error, fabs(A(i, j) - Anew(i, j)));
-      }
-
-      // compute the overall maximum error
-      double error = reduce_max(t, local_error);
-
-      /* Fill A with the new values */
-      for (auto [i, j] : t.apply_partition(shape(A)))
-      {
-        A(i, j) = Anew(i, j);
-      }
-
-      if (iter % 25 == 0 && t.rank() == 0)
-      {
-        printf("iter %zu : error %e (tol %e)\n", iter, error, tol);
-      }
-    }
-  };
-
-  cuda_safe_call(cudaEventRecord(stop, ctx.fence()));
-
-  ctx.finalize();
-
-  float elapsedTime;
-  cudaEventElapsedTime(&elapsedTime, start, stop);
-  printf("Elapsed time: %f ms\n", elapsedTime);
-}