[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/threads/axpy-threads-graph.cu

@@ -0,0 +1,74 @@
+//===----------------------------------------------------------------------===//
+//
+// 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) 2022-2025 NVIDIA CORPORATION & AFFILIATES.
+//
+//===----------------------------------------------------------------------===//
+
+/**
+ * @file
+ *
+ * @brief Ensure a graph_ctx can be used concurrently
+ *
+ */
+
+#include <cuda/experimental/stf.cuh>
+
+#include <mutex>
+#include <thread>
+
+using namespace cuda::experimental::stf;
+
+void mytask(graph_ctx ctx, int /*id*/)
+{
+  const size_t N = 16;
+
+  int alpha = 3;
+
+  auto lX = ctx.logical_data<int>(N);
+  auto lY = ctx.logical_data<int>(N);
+
+  ctx.parallel_for(lX.shape(), lX.write(), lY.write())->*[] __device__(size_t i, auto x, auto y) {
+    x(i) = (1 + i);
+    y(i) = (2 + i * i);
+  };
+
+  /* Compute Y = Y + alpha X */
+  for (size_t i = 0; i < 200; i++)
+  {
+    ctx.parallel_for(lY.shape(), lY.rw(), lX.read())->*[alpha] __device__(size_t i, auto dY, auto dX) {
+      dY(i) += alpha * dX(i);
+    };
+  }
+
+  ctx.host_launch(lX.read(), lY.read())->*[alpha](auto x, auto y) {
+    for (size_t i = 0; i < N; i++)
+    {
+      EXPECT(x(i) == 1 + i);
+      EXPECT(y(i) == 2 + i * i + 200 * alpha * x(i));
+    }
+  };
+}
+
+int main()
+{
+  graph_ctx ctx;
+
+  ::std::vector<::std::thread> threads;
+  // Launch threads
+  for (int i = 0; i < 10; ++i)
+  {
+    threads.emplace_back(mytask, ctx, i);
+  }
+
+  // Wait for all threads to complete.
+  for (auto& th : threads)
+  {
+    th.join();
+  }
+
+  ctx.finalize();
+}