If t1 completes, trigger t2 and t3 in parallel.

When t1 finishes, you can trigger t2 and t3 concurrently by using an event-driven approach. In a simple script, you could call t2 and t3 in separate threads or async tasks after t1’s completion. For example, in Python:

import threading

def t1():
    # work
    pass

def t2():
    # work
    pass

def t3():
    # work
    pass

# run t1
threading.Thread(target=t1).start()
# after t1 finishes, start t2 and t3
# in real code, use a callback or a future

In production, a workflow engine or message queue (e.g., RabbitMQ, Kafka) can publish a completion event for t1, and consumers for t2 and t3 subscribe to that event, starting them immediately. The key idea is to decouple the trigger from the tasks and allow the system to schedule them in parallel once the prerequisite is satisfied. Complexity is O(1) for the trigger logic, and the parallel execution cost depends on the tasks themselves.