[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/interface/scalar_interface.cu

@@ -0,0 +1,72 @@
+//===----------------------------------------------------------------------===//
+//
+// 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 Ensure that the scalar data interface works on both stream and graph backends
+ *
+ */
+
+#include <cuda/experimental/stf.cuh>
+
+using namespace cuda::experimental::stf;
+
+void test_shape_from_scalar_view()
+{
+  double x = 0;
+  scalar_view<double> sv(&x);
+  shape_of<scalar_view<double>> s = shape(sv);
+  EXPECT(s.size() == sizeof(double));
+
+  size_t n = 0;
+  scalar_view<size_t> sv_n(&n);
+  shape_of<scalar_view<size_t>> s_n = shape(sv_n);
+  EXPECT(s_n.size() == sizeof(size_t));
+}
+
+template <typename Ctx>
+void run()
+{
+  Ctx ctx;
+
+  double a = 42.0;
+  double b = 12.3;
+
+  auto la = ctx.logical_data(scalar_view<double>(&a)).set_symbol("a");
+  auto lb = ctx.logical_data(scalar_view<double>(&b)).set_symbol("b");
+  auto lc = ctx.logical_data(shape_of<scalar_view<double>>()).set_symbol("c");
+
+  ctx.parallel_for(box(1), la.read(), lb.read(), lc.write())->*[] __device__(size_t, auto a, auto b, auto c) {
+    *c.addr = *a.addr + *b.addr;
+  };
+
+  ctx.host_launch(lc.read())->*[](auto x) {
+    EXPECT(fabs(*x.addr - (42.0 + 12.3)) < 0.001);
+  };
+
+  // Exercise logical_data(la.shape()) when la is scalar_view-backed (uses shape_of from scalar_view)
+  auto ld = ctx.logical_data(la.shape()).set_symbol("d");
+  ctx.parallel_for(box(1), la.read(), ld.write())->*[] __device__(size_t, auto a, auto d) {
+    *d.addr = *a.addr;
+  };
+  ctx.host_launch(ld.read())->*[](auto x) {
+    EXPECT(fabs(*x.addr - 42.0) < 0.001);
+  };
+
+  ctx.finalize();
+}
+
+int main()
+{
+  test_shape_from_scalar_view();
+  run<stream_ctx>();
+  run<graph_ctx>();
+}