[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/hashtable/fusion.cu

@@ -0,0 +1,64 @@
+//===----------------------------------------------------------------------===//
+//
+// 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/hashtable_linearprobing.cuh>
+#include <cuda/experimental/__stf/stream/stream_ctx.cuh>
+
+using namespace cuda::experimental::stf;
+
+// Iterate over every item in the hashtableA, and add them to B
+
+__global__ void gpu_merge_hashtable(hashtable A, const hashtable B)
+{
+  unsigned int threadid = blockIdx.x * blockDim.x + threadIdx.x;
+  while (threadid < reserved::kHashTableCapacity)
+  {
+    if (B.addr[threadid].key != reserved::kEmpty)
+    {
+      uint32_t value = B.addr[threadid].value;
+      if (value != reserved::kEmpty)
+      {
+        //    printf("INSERTING key %d value %d\n", pHashTableB[threadid].key, value);
+        A.insert(B.addr[threadid]);
+      }
+    }
+    threadid += blockDim.x * gridDim.x;
+  }
+}
+
+int main()
+{
+  stream_ctx ctx;
+
+  hashtable A;
+  A.insert(reserved::KeyValue(107, 4));
+  A.insert(reserved::KeyValue(108, 6));
+
+  hashtable B;
+  B.insert(reserved::KeyValue(7, 14));
+  B.insert(reserved::KeyValue(8, 16));
+
+  auto lA = ctx.logical_data(A);
+  auto lB = ctx.logical_data(B);
+
+  ctx.task(lA.rw(), lB.read())->*[](auto stream, auto hA, auto hB) {
+    gpu_merge_hashtable<<<32, 128, 0, stream>>>(hA, hB);
+    cuda_safe_call(cudaGetLastError());
+  };
+
+  ctx.host_launch(lA.read())->*[](auto hA) {
+    EXPECT(hA.get(107) == 4);
+    EXPECT(hA.get(108) == 6);
+    EXPECT(hA.get(7) == 14);
+    EXPECT(hA.get(8) == 16);
+  };
+
+  ctx.finalize();
+}