[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/cpp/reuse_computation.cu

@@ -0,0 +1,80 @@
+//===----------------------------------------------------------------------===//
+//
+// 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/utility/run_once.cuh>
+#include <cuda/experimental/stf.cuh>
+
+using namespace cuda::experimental::stf;
+
+int main()
+{
+  context ctx;
+
+  const int N  = 16;
+  size_t niter = 12;
+
+  int A[N];
+
+  for (int i = 0; i < N; i++)
+  {
+    A[i] = 2 * i + 1;
+  }
+
+  auto lres = ctx.logical_data(A);
+
+  for (size_t k = 0; k < niter; k++)
+  {
+    auto ltmp = ctx.logical_data(lres.shape());
+    ctx.parallel_for(ltmp.shape(), ltmp.write())->*[] __device__(size_t i, auto tmp) {
+      tmp(i) = i;
+    };
+
+    ctx.parallel_for(lres.shape(), ltmp.read(), lres.rw())->*[] __device__(size_t i, auto tmp, auto res) {
+      res(i) += tmp(i);
+    };
+  }
+
+  for (size_t k = 0; k < niter; k++)
+  {
+    auto ltmp = run_once()->*[&]() {
+      // Ensure this is only done once !
+      static bool done = false;
+      EXPECT(!done);
+      done = true;
+
+      auto ltmp = ctx.logical_data(lres.shape());
+      ctx.parallel_for(ltmp.shape(), ltmp.write())->*[] __device__(size_t i, auto tmp) {
+        tmp(i) = i;
+      };
+      return ltmp;
+    };
+
+    auto ltmp2 = run_once(size_t(k % 4))->*[&](size_t val) {
+      // fprintf(stderr, "COMPUTE FOR %ld\n", val);
+
+      auto ltmp = ctx.logical_data(lres.shape());
+      ctx.parallel_for(ltmp.shape(), ltmp.write())->*[val] __device__(size_t i, auto tmp) {
+        tmp(i) = val;
+      };
+      return ltmp;
+    };
+
+    ctx.parallel_for(lres.shape(), ltmp.read(), lres.rw())->*[] __device__(size_t i, auto tmp, auto res) {
+      res(i) += tmp(i);
+    };
+  }
+
+  ctx.finalize();
+
+  for (int i = 0; i < N; i++)
+  {
+    EXPECT(A[i] == (2 * i + 1) + 2 * i * niter);
+  }
+}