The interaction between a Python frontend thread and a C++ backend thread in deep learning frameworks proceeds through three distinct stages. First, the frontend directs the backend to append a computational task (e.g., y = x + 1) to its processing queue, an action requiring time $t_1$. Second, the backend extracts and executes the underlying mathematical operation, consuming time $t_2$. Third, when the computed outcome is explicitly requested (such as for printing), the backend transmits the result back to the frontend, taking time $t_3$. Under synchronous execution, every iteration must complete all three stages before the next begins, so repeating the loop $10000$ times costs approximately $10000 (t_1 + t_2 + t_3)$. Under asynchronous execution, the frontend can continuously enqueue new tasks without waiting, so the total time reduces to approximately $t_1 + 10000 t_2 + t_3$ (assuming $10000 t_2 > 9999 t_1$), because the frontend and backend operate concurrently and only the first enqueue and the final result retrieval are serialized.