[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/stress/launch_overhead.cu

@@ -0,0 +1,65 @@
+//===----------------------------------------------------------------------===//
+//
+// 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.cuh>
+
+using namespace cuda::experimental::stf;
+
+int main(int argc, char** argv)
+{
+  stream_ctx ctx;
+  const size_t N = 16;
+  double X[N], Y[N];
+
+  for (size_t i = 0; i < N; i++)
+  {
+    X[i] = 1.0;
+    Y[i] = 2.0;
+  }
+
+  auto lX = ctx.logical_data(X);
+  auto lY = ctx.logical_data(Y);
+
+#ifdef NDEBUG
+  int iter_cnt = 1000000;
+#else
+  int iter_cnt = 10000;
+  fprintf(stderr, "Warning: Running with small problem size in debug mode, should use DEBUG=0.\n");
+#endif
+
+  if (argc > 1)
+  {
+    iter_cnt = atoi(argv[1]);
+  }
+
+  std::chrono::steady_clock::time_point start, stop;
+  start = std::chrono::steady_clock::now();
+  for (int iter = 0; iter < iter_cnt; iter++)
+  {
+    ctx.launch(lX.read(), lY.rw())->*[] _CCCL_DEVICE(auto th, auto X, auto Y) {
+      for (size_t i = th.rank(); i < X.size(); i += th.size())
+      {
+        Y(i) = 2.0 * X(i);
+      }
+    };
+
+    ctx.launch(lX.rw(), lY.read())->*[] _CCCL_DEVICE(auto th, auto X, auto Y) {
+      for (size_t i = th.rank(); i < X.size(); i += th.size())
+      {
+        X(i) = 0.5 * Y(i);
+      }
+    };
+  }
+  stop = std::chrono::steady_clock::now();
+  ctx.finalize();
+
+  std::chrono::duration<double> duration = stop - start;
+  fprintf(stderr, "Elapsed: %.2lf us per task\n", duration.count() * 1000000.0 / (2 * iter_cnt));
+}