[CCCL] Add missing CCCL components: c2h, nvbench_helper, cmake, cudax, AGENTS.md

Added 863 files from NVIDIA/cccl sparse checkout:
- c2h/ (27 files): Catch2 test helpers — generators, validators, runner
- nvbench_helper/ (10 files): Benchmark harness utilities
- cmake/ (29 files): CMake presets and build helpers
- cudax/ (794 files): Experimental CUDA extensions
- AGENTS.md: NVIDIA's official AI agent instructions for CCCL
- CMakePresets.json: Standardized build configurations
- cccl-version.json: Version tracking

Also added CCCL_ASSET_MAP.md mapping all 4295 CCCL files to
competition value and PRD items.

cccl_upstream now covers 100% of competition-critical assets:
- 27 tuning headers (SM80/90/100 benchmark data)
- 32 dispatch headers (algorithm implementations)
- 60 Thrust examples (correctness verification)
- 217 CUB Catch2 tests (regression matrix)
- 153 CUB benchmarks (parameter space search)
- 18 CUB examples (API verification)
- 27 test helpers + benchmark harness
- 794 cudax experimental extensions

cccl_upstream/cudax/test/common/host_device.cuh

@@ -0,0 +1,101 @@
+//===----------------------------------------------------------------------===//
+//
+// Part of CUDA Experimental 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) 2023 NVIDIA CORPORATION & AFFILIATES.
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef __COMMON_HOST_DEVICE_H__
+#define __COMMON_HOST_DEVICE_H__
+
+#include "utility.cuh"
+
+template <typename Dims, typename Lambda>
+void __global__ lambda_launcher(const Dims dims, const Lambda lambda)
+{
+  lambda(dims);
+}
+
+template <typename Comparator, unsigned int FilterArch>
+bool arch_filter(const cudaDeviceProp& props)
+{
+  int act_arch = props.major * 10 + props.minor;
+  if (Comparator()(act_arch, FilterArch))
+  {
+    return true;
+  }
+  return false;
+}
+
+static bool skip_host_exec(bool (* /* filter */)(const cudaDeviceProp&))
+{
+  return false;
+}
+
+static bool skip_device_exec(bool (*filter)(const cudaDeviceProp&))
+{
+  cudaDeviceProp props;
+  REQUIRE_CUDART(cudaGetDeviceProperties(&props, 0));
+  return filter(props);
+}
+
+template <typename Dims, typename Lambda, typename... Filters>
+void test_host_dev(const Dims& dims, const Lambda& lambda, const Filters&... filters)
+{
+  SECTION("Host execution")
+  {
+    if ((... && !skip_host_exec(filters)))
+    {
+      // host testing
+      lambda(dims);
+    }
+  }
+
+  SECTION("Device execution")
+  {
+    // Asymmetrical but cleaner
+    if ((... || skip_device_exec(filters)))
+    {
+      return;
+    }
+
+    cudaLaunchConfig_t config = {};
+    config.gridDim            = {0};
+    cudaLaunchAttribute attrs[1];
+    config.attrs = &attrs[0];
+
+    config.blockDim = dims.extents(cuda::gpu_thread, cuda::block);
+    config.gridDim  = dims.extents(cuda::block, cuda::grid);
+
+    if constexpr (Dims::has_level(cluster))
+    {
+      dim3 cluster_dims                            = dims.extents(cuda::block, cuda::cluster);
+      config.attrs[config.numAttrs].id             = cudaLaunchAttributeClusterDimension;
+      config.attrs[config.numAttrs].val.clusterDim = {cluster_dims.x, cluster_dims.y, cluster_dims.z};
+      config.numAttrs                              = 1;
+    }
+    else
+    {
+      config.numAttrs = 0;
+    }
+
+    // device testing
+    REQUIRE_CUDART(cudaLaunchKernelEx(&config, lambda_launcher<Dims, Lambda>, dims, lambda));
+    REQUIRE_CUDART(cudaDeviceSynchronize());
+  }
+}
+
+template <typename Fn, typename Tuple>
+void apply_each(const Fn& fn, const Tuple& tuple)
+{
+  cuda::std::apply(
+    [&](const auto&... elems) {
+      (fn(elems), ...);
+    },
+    tuple);
+}
+
+#endif // __COMMON_HOST_DEVICE_H__