[INFRA] Import NVIDIA/CCCL upstream as optimization reference library

CCCL (CUDA C++ Core Libraries) provides:
- CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk)
- Thrust: high-level parallel algorithms (transform_reduce, sort, scan)
- libcudacxx: CUDA C++ standard library (atomics, barriers, memory)
- cudax: experimental features (memory resources, allocators)
- Tuning policies: per-SM hardware-specific algorithm parameters

Competition optimization vectors mapped to CCCL:
- Output TPS (83% weight): warp_reduce, block_reduce, device_topk
- Input TPS (14% weight): device_scan, block_load, prefetch
- Cache TPS (3% weight): prefix caching strategy patterns
- Memory (0.9 util): pooled/cached/buddy allocators

Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only)
License: Apache-2.0

cccl_upstream/cudax/test/stf/graph/epoch.cu

@@ -0,0 +1,56 @@
+//===----------------------------------------------------------------------===//
+//
+// 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 Test explicit uses of the API to change stage and create a sequence
+ *        of CUDA graphs
+ */
+
+#include <cuda/experimental/__stf/graph/graph_ctx.cuh>
+
+using namespace cuda::experimental::stf;
+
+int main()
+{
+  graph_ctx ctx;
+
+  const size_t N     = 8;
+  const size_t NITER = 2;
+
+  double A[N];
+  for (size_t i = 0; i < N; i++)
+  {
+    A[i] = 1.0 * i;
+  }
+
+  auto lA = ctx.logical_data(A);
+
+  for (size_t k = 0; k < NITER; k++)
+  {
+    ctx.parallel_for(blocked_partition(), exec_place::current_device(), lA.shape(), lA.rw())
+        ->*[] __host__ __device__(size_t i, slice<double> A) { A(i) = cos(A(i)); };
+
+    ctx.change_stage();
+  }
+
+  ctx.finalize();
+
+  for (size_t i = 0; i < N; i++)
+  {
+    double Ai_ref = 1.0 * i;
+    for (size_t k = 0; k < NITER; k++)
+    {
+      Ai_ref = cos(Ai_ref);
+    }
+
+    EXPECT(fabs(A[i] - Ai_ref) < 0.01);
+  }
+}