Use gRPC to handle communication and data transmission between object manager (#4996)

python/ray/tests/perf_integration_tests/test_perf_integration.py

@@ -72,3 +72,31 @@ def test_task_forward(benchmark, num_tasks):
         # Warm up
         ray.get([f.remote() for _ in range(100)])
         benchmark(benchmark_task_forward, f, num_tasks)
+
+
+def benchmark_transfer_object(actor, object_ids):
+    ray.get(actor.f.remote(object_ids))
+
+
+@pytest.mark.benchmark
+@pytest.mark.parametrize("object_number, data_size",
+                         [(10000, 500), (10000, 5000), (1000, 500),
+                          (1000, 5000)])
+def test_transfer_performance(benchmark, ray_start_cluster_head, object_number,
+                              data_size):
+    cluster = ray_start_cluster_head
+    cluster.add_node(resources={"my_resource": 1}, object_store_memory=10**9)
+
+    @ray.remote(resources={"my_resource": 1})
+    class ObjectActor:
+        def f(self, object_ids):
+            ray.get(object_ids)
+
+    # setup remote actor
+    actor = ObjectActor.remote()
+    actor.f.remote([])
+
+    data = bytes(1) * data_size
+    object_ids = [ray.put(data) for _ in range(object_number)]
+
+    benchmark(benchmark_transfer_object, actor, object_ids)