[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/interface/scalar_interface.cu

@@ -0,0 +1,72 @@
+//===----------------------------------------------------------------------===//
+//
+// 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 Ensure that the scalar data interface works on both stream and graph backends
+ *
+ */
+
+#include <cuda/experimental/stf.cuh>
+
+using namespace cuda::experimental::stf;
+
+void test_shape_from_scalar_view()
+{
+  double x = 0;
+  scalar_view<double> sv(&x);
+  shape_of<scalar_view<double>> s = shape(sv);
+  EXPECT(s.size() == sizeof(double));
+
+  size_t n = 0;
+  scalar_view<size_t> sv_n(&n);
+  shape_of<scalar_view<size_t>> s_n = shape(sv_n);
+  EXPECT(s_n.size() == sizeof(size_t));
+}
+
+template <typename Ctx>
+void run()
+{
+  Ctx ctx;
+
+  double a = 42.0;
+  double b = 12.3;
+
+  auto la = ctx.logical_data(scalar_view<double>(&a)).set_symbol("a");
+  auto lb = ctx.logical_data(scalar_view<double>(&b)).set_symbol("b");
+  auto lc = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("c");
+
+  ctx.parallel_for(box(1), la.read(), lb.read(), lc.write())->*[] __device__(size_t, auto a, auto b, auto c) {
+    *c.addr = *a.addr + *b.addr;
+  };
+
+  ctx.host_launch(lc.read())->*[](auto x) {
+    EXPECT(fabs(*x.addr - (42.0 + 12.3)) < 0.001);
+  };
+
+  // Exercise logical_data(la.shape()) when la is scalar_view-backed (uses shape_of from scalar_view)
+  auto ld = ctx.logical_data(la.shape()).set_symbol("d");
+  ctx.parallel_for(box(1), la.read(), ld.write())->*[] __device__(size_t, auto a, auto d) {
+    *d.addr = *a.addr;
+  };
+  ctx.host_launch(ld.read())->*[](auto x) {
+    EXPECT(fabs(*x.addr - 42.0) < 0.001);
+  };
+
+  ctx.finalize();
+}
+
+int main()
+{
+  test_shape_from_scalar_view();
+  run<stream_ctx>();
+  run<graph_ctx>();
+}