[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/examples/stf/09-dot-reduce.cu

@@ -0,0 +1,55 @@
+//===----------------------------------------------------------------------===//
+//
+// 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 Implementation of the DOT kernel using a reduce access mode
+ *
+ */
+
+#include <cuda/experimental/stf.cuh>
+
+using namespace cuda::experimental::stf;
+
+int main()
+{
+  const size_t N = 16;
+  double X[N], Y[N];
+
+  double ref_res = 0.0;
+
+  for (size_t i = 0; i < N; i++)
+  {
+    X[i] = cos(double(i));
+    Y[i] = sin(double(i));
+
+    // Compute the reference result of the DOT product of X and Y
+    ref_res += X[i] * Y[i];
+  }
+
+  context ctx;
+  auto lX = ctx.logical_data(X);
+  auto lY = ctx.logical_data(Y);
+
+  auto lsum = ctx.logical_data(shape_of<scalar_view<double>>());
+
+  /* Compute sum(x_i * y_i)*/
+  ctx.parallel_for(lY.shape(), lX.read(), lY.read(), lsum.reduce(reducer::sum<double>{}))
+      ->*[] __device__(size_t i, auto dX, auto dY, double& sum) {
+            sum += dX(i) * dY(i);
+          };
+
+  double res = ctx.wait(lsum);
+
+  ctx.finalize();
+
+  _CCCL_ASSERT(fabs(res - ref_res) < 0.0001, "Invalid result");
+}