diff --git a/examples/python_naive_matmul/1_naive_matmul.py b/examples/python_naive_matmul/1_naive_matmul.py
new file mode 100644
index 000000000..e7fe092a8
--- /dev/null
+++ b/examples/python_naive_matmul/1_naive_matmul.py
@@ -0,0 +1,96 @@
+import time
+
+import kp
+import numpy as np
+
+
+class MatMulOp:
+    def __init__(self, manager: kp.Manager, local_size_x: int = 1, local_size_y: int = 1):
+        self.mgr = manager
+        assert(local_size_x > 0)
+        assert(local_size_y > 0)
+        self.local_size_x = local_size_x
+        self.local_size_y = local_size_y
+
+        self.shader = kp.Shader.compile_source(f'''
+    #version 450
+
+    layout (local_size_x = {local_size_x}, local_size_y = {local_size_y}) in;
+
+    layout (set = 0, binding = 0) readonly buffer buf_in_tensor_1 {{ float in_tensor_1[]; }};
+    layout (set = 0, binding = 1) readonly buffer buf_in_tensor_2 {{ float in_tensor_2[]; }};
+    layout (set = 0, binding = 2) writeonly buffer buf_out_tensor {{ float out_tensor[]; }};
+
+    layout (constant_id = 0) const float tensor_size_f = 0;
+
+
+    void main()
+    {{
+        uint globalRow = gl_GlobalInvocationID.x;
+        uint globalCol = gl_GlobalInvocationID.y;
+        uint tensor_size = uint(tensor_size_f);
+        float acc = 0.0;
+        for(uint k = 0u; k < tensor_size; k++)
+            acc += in_tensor_1[(k * tensor_size) + globalRow] * in_tensor_2[(globalCol * tensor_size) + k];
+        out_tensor[(globalCol * tensor_size) + globalRow] = acc;
+    }}''')
+        self.tensor_shape: tuple[int, int] = (0, 0)
+        self.params: list[kp.Tensor] = []
+        self.algo = None
+
+    def __call__(self, tensor_shape: tuple[int, int], tensor_in_1: kp.Tensor, tensor_in_2: kp.Tensor,
+                 tensor_out: kp.Tensor):
+        params = [tensor_in_1, tensor_in_2, tensor_out]
+
+        if self.algo is None or self.tensor_shape != tensor_shape or self.params != params:
+            self.tensor_shape = tensor_shape
+            self.params = params
+            self.algo = self.mgr.algorithm(
+                params,  # params
+                self.shader,  # spirv
+                (tensor_shape[0] // self.local_size_x, tensor_shape[1] // self.local_size_y, 1),  # workgroup
+                [float(tensor_shape[0])],  # spec_consts
+                [])  # push_consts
+
+        (self.mgr.sequence()
+         .record(kp.OpTensorSyncDevice(self.params))
+         .record(kp.OpAlgoDispatch(self.algo))
+         .record(kp.OpTensorSyncLocal(self.params))
+         .eval())
+
+
+def main():
+    mgr = kp.Manager()
+
+    matmul_op = MatMulOp(mgr, 64, 64)
+
+    tensor_size = 512
+    tensor_shape = [tensor_size, tensor_size]
+    tensor_in_1 = mgr.tensor(np.triu(np.ones(tensor_shape)))
+    tensor_in_2 = mgr.tensor(np.triu(np.ones(tensor_shape)))
+    tensor_out = mgr.tensor(np.zeros(tensor_shape))
+
+    print(f'{tensor_shape} input tensors:\n'
+          f'{tensor_in_1.data().reshape(tensor_shape)}\n'
+          f'{tensor_in_2.data().reshape(tensor_shape)}\n')
+
+    matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
+
+    experiment_count = 1000
+    start_time = time.time()
+    for _ in range(experiment_count):
+        matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
+    end_time = time.time()
+    experiment_time = end_time - start_time
+    op_count = tensor_shape[0] * tensor_shape[1] * (tensor_shape[1] - 1)
+
+    print(f'Output :\n{tensor_out.data().reshape(tensor_shape)}')
+
+    print(f'{experiment_count} matmul time : '
+          f'{experiment_time * 1000:0.2f}ms => '
+          f'{experiment_count / experiment_time:0.2f}op/s or '
+          f'{experiment_count * op_count / (1e9 * experiment_time):0.2f}GFLOPS')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/examples/python_naive_matmul/matmul.py b/examples/python_naive_matmul/first_example.py
similarity index 100%
rename from examples/python_naive_matmul/matmul.py
rename to examples/python_naive_matmul/first_example.py