[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/examples/01-axpy-launch-ranges-cg.cu

@@ -0,0 +1,75 @@
+//===----------------------------------------------------------------------===//
+//
+// 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/__places/partitions/blocked_partition.cuh>
+#include <cuda/experimental/__places/partitions/cyclic_shape.cuh>
+#include <cuda/experimental/stf.cuh>
+
+using namespace cuda::experimental::stf;
+
+double X0(int i)
+{
+  return sin((double) i);
+}
+
+double Y0(int i)
+{
+  return cos((double) i);
+}
+
+int main()
+{
+  stream_ctx ctx;
+
+  const int N = 128;
+  double X[N], Y[N];
+
+  for (int ind = 0; ind < N; ind++)
+  {
+    X[ind] = X0(ind);
+    Y[ind] = Y0(ind);
+  }
+
+  const double alpha = 3.14;
+
+  auto handle_X = ctx.logical_data(X, {N});
+  auto handle_Y = ctx.logical_data(Y, {N});
+
+  auto number_devices = 4;
+  auto all_devs       = exec_place::repeat(exec_place::device(0), number_devices);
+
+  auto spec = par(16 * 4, par(4));
+  ctx.launch(spec, all_devs, handle_X.read(), handle_Y.rw())->*[=] _CCCL_DEVICE(auto th, auto x, auto y) {
+    // Blocked partition among elements in the outer most level
+    auto outer_sh = blocked_partition::apply(shape(x), pos4(th.rank(0)), dim4(th.size(0)));
+
+    // Cyclic partition among elements in the remaining levels
+    auto inner_sh = cyclic_partition::apply(outer_sh, pos4(th.inner().rank()), dim4(th.inner().size()));
+
+    for (auto ind : inner_sh)
+    {
+      y(ind) += alpha * x(ind);
+    }
+  };
+
+  ctx.host_launch(handle_X.read(), handle_Y.read())->*[=](auto X, auto Y) {
+    for (int ind = 0; ind < N; ind++)
+    {
+      // Y should be Y0 + alpha X0
+      // fprintf(stderr, "Y[%ld] = %lf - expect %lf\n", ind, Y(ind), (Y0(ind) + alpha * X0(ind)));
+      EXPECT(fabs(Y(ind) - (Y0(ind) + alpha * X0(ind))) < 0.0001);
+
+      // X should be X0
+      EXPECT(fabs(X(ind) - X0(ind)) < 0.0001);
+    }
+  };
+
+  ctx.finalize();
+}