diff --git a/tests/st/nopnode/test_eliminate_nopnode.py b/tests/st/runtime/nopnode/test_eliminate_nopnode.py
similarity index 100%
rename from tests/st/nopnode/test_eliminate_nopnode.py
rename to tests/st/runtime/nopnode/test_eliminate_nopnode.py
diff --git a/tests/st/nopnode/test_runtime_optimize.py b/tests/st/runtime/nopnode/test_runtime_optimize.py
similarity index 100%
rename from tests/st/nopnode/test_runtime_optimize.py
rename to tests/st/runtime/nopnode/test_runtime_optimize.py
diff --git a/tests/st/runtime/test_runtime_optimize.py b/tests/st/runtime/test_runtime_optimize.py
new file mode 100644
index 00000000000..1ce3c3b7bf1
--- /dev/null
+++ b/tests/st/runtime/test_runtime_optimize.py
@@ -0,0 +1,194 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+import numpy as np
+import pytest
+import mindspore
+from mindspore import context, ops, nn, Tensor
+
+
+class NetNonConcurrent(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.relu = ops.ReLU()
+        self.add = ops.Add()
+
+    def construct(self, input_x, input_x1, input_x2, input_x3):
+        output = self.relu(input_x)
+        for _ in range(50):
+            output = self.add(output, 1)
+        for _ in range(50):
+            output = self.add(output, 1)
+        for _ in range(50):
+            output = self.add(output, 1)
+        for _ in range(50):
+            output = self.add(output, 1)
+        return output
+
+
+class NetNonConcurrentWithWhile(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.relu = ops.ReLU()
+        self.add = ops.Add()
+
+    def construct(self, input_x, input_loop, input_x1, input_loop1):
+        output = self.relu(input_x)
+        while input_loop < 4:
+            input_loop = input_loop + 1
+            for _ in range(50):
+                output = self.add(output, 1)
+        return output
+
+
+class NetConcurrent(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.relu = ops.ReLU()
+        self.add = ops.Add()
+
+    def construct(self, input_x1, input_x2, input_x3, input_x4):
+        output1 = self.relu(input_x1)
+        output2 = self.relu(input_x2)
+        output3 = self.relu(input_x3)
+        output4 = self.relu(input_x4)
+        for _ in range(50):
+            output1 = self.add(output1, 1)
+        for _ in range(50):
+            output2 = self.add(output2, 1)
+        for _ in range(50):
+            output3 = self.add(output3, 1)
+        for _ in range(50):
+            output4 = self.add(output4, 1)
+
+        output = output1 + output2 + output3 + output4
+        return output
+
+
+class NetConcurrentWithWhile(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.relu = ops.ReLU()
+        self.add = ops.Add()
+
+    def construct(self, input_x1, input_loop1, input_x2, input_loop2):
+        output1 = self.relu(input_x1)
+        while input_loop1 < 2:
+            input_loop1 = input_loop1 + 1
+            for _ in range(50):
+                output1 = self.add(output1, 1)
+
+        output2 = self.relu(input_x2)
+        while input_loop2 < 2:
+            input_loop2 = input_loop2 + 1
+            for _ in range(50):
+                output2 = self.add(output2, 1)
+
+        output = output1 + output2
+        return output
+
+
+def run_multi_actor_fusion(net_name, net, input1, input2, input3, input4, expect_output):
+    context.set_context(mode=context.GRAPH_MODE)
+    total_time = 0
+    total_count = 0
+    for i in range(200):
+        time1 = time.time()
+        output = net(input1, input2, input3, input4).asnumpy()
+        time2 = time.time()
+        if i > 1:
+            total_count += 1
+            total_time += (time2 - time1) * 1000
+        assert (output == expect_output).all()
+    print(net_name + " avg_time:", total_time/total_count)
+
+
+@pytest.mark.level1
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_non_concurrent():
+    """
+    Feature: Multi actor fusion with non concurrent.
+    Description: Test the net which is non concurrent, that can trigger the function of multi actor fusion.
+    Expectation: The value and shape of output are the expected values.
+    """
+    input_x = Tensor(np.ones(2), mindspore.float32)
+    net = NetNonConcurrent()
+    expect = np.array([201, 201])
+    run_multi_actor_fusion("non_concurrent", net, input_x, input_x, input_x, input_x, expect)
+
+
+@pytest.mark.level1
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_non_concurrent_with_while():
+    """
+    Feature: Multi actor fusion with non concurrent and while.
+    Description: Test the net which is non concurrent with while, that can trigger the function of multi actor fusion.
+    Expectation: The value and shape of output are the expected values.
+    """
+    input_x = Tensor(np.ones(2), mindspore.float32)
+    input_loop = Tensor([0], mindspore.float32)
+    net = NetNonConcurrentWithWhile()
+    expect = np.array([201, 201])
+    run_multi_actor_fusion("non_concurrent_with_while", net, input_x, input_loop, input_x, input_loop, expect)
+
+
+@pytest.mark.level1
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_concurrent():
+    """
+    Feature: Multi actor fusion with concurrent.
+    Description: Test the net which is concurrent, that can trigger the function of multi actor fusion.
+    Expectation: The value and shape of output are the expected values.
+    """
+    input1 = Tensor(np.ones(2), mindspore.float32)
+    input2 = Tensor(np.ones(2), mindspore.float32)
+    input3 = Tensor(np.ones(2), mindspore.float32)
+    input4 = Tensor(np.ones(2), mindspore.float32)
+    net = NetConcurrent()
+    expect = np.array([204, 204])
+    run_multi_actor_fusion("concurrent", net, input1, input2, input3, input4, expect)
+
+
+@pytest.mark.level1
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_concurrent_with_while():
+    """
+    Feature: Multi actor fusion with concurrent and while.
+    Description: Test the net which is concurrent with while, that can trigger the function of multi actor fusion.
+    Expectation: The value and shape of output are the expected values.
+    """
+    input1 = Tensor(np.ones(2), mindspore.float32)
+    input_loop1 = Tensor([0], mindspore.float32)
+    input2 = Tensor(np.ones(2), mindspore.float32)
+    input_loop2 = Tensor([0], mindspore.float32)
+    net = NetConcurrentWithWhile()
+    expect = np.array([202, 202])
+    run_multi_actor_fusion("concurrent_with_while", net, input1, input_loop1, input2, input_loop2, expect)