[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/examples/stf/04-fibonacci.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.
+//
+//===----------------------------------------------------------------------===//
+
+/**
+ * @file
+ *
+ * @brief An example of Fibonacci sequence illustrating how we can use
+ *        dynamically created logical data
+ */
+
+#include <cuda/experimental/stf.cuh>
+
+using namespace cuda::experimental::stf;
+
+int fibo_ref(int n)
+{
+  if (n < 2)
+  {
+    return n;
+  }
+  else
+  {
+    return fibo_ref(n - 1) + fibo_ref(n - 2);
+  }
+}
+
+__global__ void add(slice<int> out, const slice<const int> in1, const slice<const int> in2)
+{
+  out(0) = in1(0) + in2(0);
+}
+
+__global__ void set(slice<int> out, int val)
+{
+  out(0) = val;
+}
+
+logical_data<slice<int>> compute_fibo(context& ctx, int n)
+{
+  auto out = ctx.logical_data(shape_of<slice<int>>(1));
+  if (n < 2)
+  {
+    ctx.task(out.write())->*[=](cudaStream_t s, auto sout) {
+      set<<<1, 1, 0, s>>>(sout, n);
+    };
+  }
+  else
+  {
+    auto fib1 = compute_fibo(ctx, n - 1);
+    auto fib2 = compute_fibo(ctx, n - 2);
+    ctx.task(fib1.read(), fib2.read(), out.write())->*[=](cudaStream_t s, auto s1, auto s2, auto sout) {
+      add<<<1, 1, 0, s>>>(sout, s1, s2);
+    };
+  }
+
+  return out;
+}
+
+int main(int argc, char** argv)
+{
+  int n = (argc > 1) ? atoi(argv[1]) : 4;
+
+  context ctx; // = graph_ctx();
+  auto result = compute_fibo(ctx, n);
+  ctx.host_launch(result.read())->*[&](auto res) {
+    EXPECT(res(0) == fibo_ref(n));
+  };
+  ctx.finalize();
+}