Coordinate Tasks. Run Threads. Control Execution.

Three progressive missions using the KASA Python Runtime.

Visualize Concurrency

Concurrency manages multiple tasks whose lifetimes overlap. Drag each scenario to the execution model that best describes it.

A → B → C, finish one by one Download while UI remains responsive Two CPU workers execute chunks simultaneously

Drag all three cards.

① Sequential

② Concurrent / Interleaving

③ Parallel

Python threads are commonly useful for I/O-bound work: while one thread waits, another can make progress.

Thread(target, args) → start() → join(). Complete the TODOs and run the editable code.

Ready.

Main

Thread-A

Thread-B

Run the simulation: Main starts workers → A/B overlap → Main waits at join() → complete.

start() begins thread execution. join() waits until that thread finishes.

Extend threading.Thread. The SYSTEM CALL is fixed—make your class fit the required interface without changing the caller.

Target: Worker-A -> load Worker-A -> process Worker-A -> save SYSTEM: completed

1 · Extend
WorkerThread(threading.Thread)

2 · Initialize
super().__init__() + store worker_name/jobs

3 · Override run()
Put work executed by start() here

4 · Preserve Caller
Do not modify the supplied SYSTEM CALL

Implement the class, then validate.