[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/reductions/slice2d_reduction.cu

@@ -0,0 +1,59 @@
+//===----------------------------------------------------------------------===//
+//
+// 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/stream/interfaces/slice_reduction_ops.cuh>
+#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
+
+using namespace cuda::experimental::stf;
+
+__global__ void add(slice<int, 2> s, int val)
+{
+  size_t tid      = threadIdx.x + blockIdx.x * blockDim.x;
+  size_t nthreads = blockDim.x * gridDim.x;
+
+  for (size_t j = 0; j < s.extent(1); j++)
+  {
+    for (size_t i = tid; i < s.extent(0); i += nthreads)
+    {
+      s(i, j) += val;
+    }
+  }
+}
+
+int main()
+{
+  stream_ctx ctx;
+
+  int array[6] = {0, 1, 2, 3, 4, 5};
+
+  // auto handle = ctx.logical_data(slice<int, 2>(&array[0], std::tuple{ 2, 3 }, 2));
+  auto handle = ctx.logical_data(make_slice(&array[0], std::tuple{2, 3}, 2));
+
+  auto redux_op = std::make_shared<slice_reduction_op_sum<int, 2>>();
+
+  ctx.task(handle.relaxed(redux_op))->*[](auto stream, auto s) {
+    add<<<32, 32, 0, stream>>>(s, 42);
+  };
+
+  ctx.task(exec_place::host(), handle.read())->*[](auto stream, auto s) {
+    cuda_safe_call(cudaStreamSynchronize(stream));
+
+    for (size_t j = 0; j < s.extent(1); j++)
+    {
+      for (size_t i = 0; i < s.extent(0); i++)
+      {
+        // fprintf(stderr, "%d\t", s(i, j));
+      }
+      // fprintf(stderr, "\n");
+    }
+  };
+
+  ctx.finalize();
+}