Concurrent and Parallel Programming in Python - Multiprocessing Introduction

The video tutorial explains the concepts of threading and multiprocessing in Python. It begins with a simple threading script to monitor CPU usage and then transitions to multiprocessing to demonstrate performance improvements. The tutorial highlights the differences in CPU utilization and speed between threading and multiprocessing, emphasizing the benefits of using multiprocessing for CPU-intensive tasks. The video concludes with a comparison of the two approaches, noting the impact of available CPU cores on performance.

What is the primary purpose of the threading script introduced in the first section?

• To monitor network usage

• To check values in a list using multiple threads

• To handle file I/O operations

• To perform database operations

How many threads are created in the threading implementation?

• 5

• 4

• 2

• 3

What is the main difference between threading and multiprocessing as discussed in the third section?

• Threading is faster than multiprocessing

• Multiprocessing can utilize multiple CPU cores

• Threading is used for GPU tasks

• Multiprocessing is only for network operations

What library is used for multiprocessing in the script?

• concurrent

• multiprocessing

• sys

• os

Why does multiprocessing provide a speed improvement over threading?

• It uses less memory

• It can execute processes on different cores

• It is easier to implement

• It requires fewer lines of code

What is the role of the global interpreter lock in threading?

• It enhances CPU utilization

• It allows for better memory management

• It prevents multiple threads from executing simultaneously

• It speeds up network operations

In what scenario is threading preferred over multiprocessing?

• When tasks require high memory usage

• When tasks are CPU-intensive

• When tasks are limited by network I/O

• When tasks involve complex calculations