[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/stf/slice/pinning.cu

@@ -0,0 +1,59 @@
+//===----------------------------------------------------------------------===//
+//
+// 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.
+//
+//===----------------------------------------------------------------------===//
+
+#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
+
+#include <iostream>
+
+using namespace cuda::experimental::stf;
+
+int main()
+{
+  stream_ctx ctx;
+
+  // Contiguous 1D
+  double* X     = new double[1024];
+  auto handle_X = ctx.logical_data(make_slice(X, 1024));
+
+  // Contiguous 2D
+  double* X2     = new double[1024 * 1024];
+  auto handle_X2 = ctx.logical_data(make_slice(X2, std::tuple{1024, 1024}, 1024));
+
+  // Contiguous 3D
+  double* X4     = new double[128 * 128 * 128];
+  auto handle_X4 = ctx.logical_data(make_slice(X4, std::tuple{128, 128, 128}, 128, 128 * 128));
+
+  // Discontiguous 2D
+  double* X3     = new double[128 * 8];
+  auto handle_X3 = ctx.logical_data(make_slice(X3, std::tuple{64, 8}, 128));
+
+  // Discontiguous 3D
+  double* X5     = new double[32 * 4 * 4];
+  auto handle_X5 = ctx.logical_data(make_slice(X5, std::tuple{16, 4, 4}, 32, 32 * 4));
+
+  double* X6     = new double[32 * 4 * 4];
+  auto handle_X6 = ctx.logical_data(make_slice(X6, std::tuple{32, 2, 4}, 32, 32 * 4));
+
+  double* X7 = new double[128 * 128 * 128];
+  cuda_safe_call(cudaHostRegister(X7, 128 * 128 * 128, cudaHostRegisterPortable));
+  auto handle_X7 = ctx.logical_data(make_slice(X7, std::tuple{128, 128, 128}, 128, 128 * 128));
+
+  // Detect that this was already pinned
+  double* X9 = new double[1024];
+  cuda_safe_call(cudaHostRegister(X9, 1024, cudaHostRegisterPortable));
+  auto handle_X9 = ctx.logical_data(make_slice(X9, 1024));
+
+  // Detect that this was already pinned
+  double* X8 = new double[4 * 4 * 4];
+  cuda_safe_call(cudaHostRegister(X8, 4 * 4 * 4, cudaHostRegisterPortable));
+  auto handle_X8 = ctx.logical_data(make_slice(X8, std::tuple{1, 4, 4}, 4, 4 * 4));
+
+  ctx.finalize();
+}