[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/08-cub-reduce.cu

@@ -0,0 +1,95 @@
+//===----------------------------------------------------------------------===//
+//
+// 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 Example of reduction implementing using CUB kernels
+ */
+
+#include <thrust/device_vector.h>
+
+#include <cuda/experimental/stf.cuh>
+
+using namespace cuda::experimental::stf;
+
+template <int BLOCK_THREADS, typename T>
+__global__ void reduce(slice<const T> values, slice<T> partials, size_t nelems)
+{
+  using namespace cub;
+  typedef BlockReduce<T, BLOCK_THREADS> BlockReduceT;
+
+  auto thread_id = BLOCK_THREADS * blockIdx.x + threadIdx.x;
+
+  // Local reduction
+  T local_sum = 0;
+  for (size_t ind = thread_id; ind < nelems; ind += blockDim.x * gridDim.x)
+  {
+    local_sum += values(ind);
+  }
+
+  __shared__ typename BlockReduceT::TempStorage temp_storage;
+
+  // Per-thread tile data
+  T result = BlockReduceT(temp_storage).Sum(local_sum);
+
+  if (threadIdx.x == 0)
+  {
+    partials(blockIdx.x) = result;
+  }
+}
+
+template <typename Ctx>
+void run()
+{
+  Ctx ctx;
+
+  const size_t N          = 1024 * 16;
+  const size_t BLOCK_SIZE = 128;
+  const size_t num_blocks = 32;
+
+  int *X, ref_tot;
+
+  X       = new int[N];
+  ref_tot = 0;
+
+  for (size_t ind = 0; ind < N; ind++)
+  {
+    X[ind] = rand() % N;
+    ref_tot += X[ind];
+  }
+
+  auto values   = ctx.logical_data(X, {N});
+  auto partials = ctx.logical_data(shape_of<slice<int>>(num_blocks));
+  auto result   = ctx.logical_data(shape_of<slice<int>>(1));
+
+  ctx.task(values.read(), partials.write(), result.write())->*[&](auto stream, auto values, auto partials, auto result) {
+    // reduce values into partials
+    reduce<BLOCK_SIZE, int><<<num_blocks, BLOCK_SIZE, 0, stream>>>(values, partials, N);
+
+    // reduce partials on a single block into result
+    reduce<BLOCK_SIZE, int><<<1, BLOCK_SIZE, 0, stream>>>(partials, result, num_blocks);
+  };
+
+  ctx.host_launch(result.read())->*[&](auto p) {
+    if (p(0) != ref_tot)
+    {
+      fprintf(stderr, "INCORRECT RESULT: p sum = %d, ref tot = %d\n", p(0), ref_tot);
+      abort();
+    }
+  };
+
+  ctx.finalize();
+}
+
+int main()
+{
+  run<stream_ctx>();
+  run<graph_ctx>();
+}