Answers

1. Threads are lightweight and enable multithreading, where we can run multiple tasks that could have idle time. A process is more expensive, enabling us to run multiple CPU-heavy tasks at the same time. Processes do not share memory, while threads do.
2. Multithreading would not speed up our Fibonacci sequence calculations because calculating Fibonacci numbers is a CPU-heavy task that does not have any idle time; therefore, the threads would run sequentially in Python. However, we did demonstrate that Rust can run multiple threads at the same time, getting a significant speed increase.
3. Multiprocessing is expensive and the processes do not share memory, making the implementation potentially more complex. A processing pool keeps the multiprocessing part of a program to a minimum. This approach also enables us to easily control the different numbers of workers we need as they're all in one place, and we can also return all the outcomes in the same sequence as they...