[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/examples/05-stencil-places.cu

@@ -0,0 +1,92 @@
+//===----------------------------------------------------------------------===//
+//
+// 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/__places/partitions/tiled_partition.cuh>
+#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
+
+using namespace cuda::experimental::stf;
+
+template <typename T>
+__global__ void stencil_kernel(slice<T> Un, slice<const T> Un1)
+{
+  size_t N = Un.extent(0);
+  for (size_t i = threadIdx.x + blockIdx.x * blockDim.x; i < N; i += blockDim.x * gridDim.x)
+  {
+    Un(i) = 0.9 * Un1(i) + 0.05 * Un1((i + N - 1) % N) + 0.05 * Un1((i + 1) % N);
+  }
+}
+
+int main(int argc, char** argv)
+{
+  stream_ctx ctx;
+
+  int NITER               = 500;
+  int NBLOCKS             = 20;
+  const size_t BLOCK_SIZE = 2048 * 1024;
+
+  if (argc > 1)
+  {
+    NITER = atoi(argv[1]);
+  }
+
+  if (argc > 2)
+  {
+    NBLOCKS = atoi(argv[2]);
+  }
+
+  const size_t TOTAL_SIZE = NBLOCKS * BLOCK_SIZE;
+
+  double* Un  = new double[TOTAL_SIZE];
+  double* Un1 = new double[TOTAL_SIZE];
+
+  for (size_t idx = 0; idx < TOTAL_SIZE; idx++)
+  {
+    Un[idx]  = (idx == 0) ? 1.0 : 0.0;
+    Un1[idx] = Un[idx];
+  }
+
+  auto lUn  = ctx.logical_data(make_slice(Un, TOTAL_SIZE));
+  auto lUn1 = ctx.logical_data(make_slice(Un1, TOTAL_SIZE));
+
+  //    std::shared_ptr<execution_grid> all_devs = exec_place::all_devices();
+  // use grid [ 0 0 0 0 ] for debugging purpose
+  auto all_devs = exec_place::repeat(exec_place::device(0), 4);
+
+  data_place cdp = data_place::composite(tiled_partition<BLOCK_SIZE>(), all_devs);
+
+  for (int iter = 0; iter < NITER; iter++)
+  {
+    // UPDATE Un from Un1
+    ctx.task(lUn.rw(cdp), lUn1.read(cdp))->*[&](auto stream, auto sUn, auto sUn1) {
+      stencil_kernel<double><<<32, 128, 0, stream>>>(sUn, sUn1);
+    };
+
+    // We make sure that the total sum of elements remains constant
+    if (iter % 250 == 0)
+    {
+      double sum = 0.0;
+
+      ctx.task(exec_place::host(), lUn.read())->*[&](auto stream, auto sUn) {
+        cuda_safe_call(cudaStreamSynchronize(stream));
+        for (size_t offset = 0; offset < TOTAL_SIZE; offset++)
+        {
+          sum += sUn(offset);
+        }
+      };
+
+      // TODO add an assertion to check whether sum is close enough to 1.0
+      // fprintf(stderr, "iter %d : CHECK SUM = %e\n", iter, sum);
+    }
+
+    std::swap(lUn, lUn1);
+  }
+
+  ctx.finalize();
+}