[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/scal.cu

@@ -0,0 +1,71 @@
+//===----------------------------------------------------------------------===//
+//
+// 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/graph/graph_ctx.cuh>
+#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
+
+using namespace cuda::experimental::stf;
+
+template <typename T>
+__global__ void scal(size_t n, T a, T* x)
+{
+  int tid      = blockIdx.x * blockDim.x + threadIdx.x;
+  int nthreads = gridDim.x * blockDim.x;
+
+  for (size_t ind = tid; ind < n; ind += nthreads)
+  {
+    x[ind] = a * x[ind];
+  }
+}
+
+double x_init(int i)
+{
+  return cos((double) i);
+}
+
+template <typename Ctx>
+void run()
+{
+  Ctx ctx;
+  const int n = 4096;
+  double X[n];
+
+  for (int ind = 0; ind < n; ind++)
+  {
+    X[ind] = x_init(ind);
+  }
+
+  auto handle_X = ctx.logical_data(X);
+
+  double alpha = 2.0;
+  int niter    = 4;
+  for (int iter = 0; iter < niter; iter++)
+  {
+    ctx.task(handle_X.rw())->*[&](cudaStream_t s, auto sX) {
+      scal<<<16, 128, 0, s>>>(sX.size(), alpha, sX.data_handle());
+    };
+  }
+
+  // Ask to use Y on the host
+  ctx.host_launch(handle_X.read())->*[&](auto sX) {
+    for (int ind = 0; ind < n; ind++)
+    {
+      EXPECT(fabs(sX(ind) - pow(alpha, niter) * (x_init(ind))) < 0.00001);
+    }
+  };
+
+  ctx.finalize();
+}
+
+int main()
+{
+  run<stream_ctx>();
+  run<graph_ctx>();
+}