Concurrent and Parallel Programming in Python - Threading, Multiprocessing, Async Introduction

The video tutorial introduces concurrent programming, highlighting its benefits over sequential programming. It explains how to utilize multiple CPU cores for efficient metric calculations and handle network IO using threads. The tutorial also covers asynchronous programming to avoid blocking, discusses Python's Global Interpreter Lock (GIL), and emphasizes thread safety. Finally, it compares threads with multiprocessing, outlining their differences and use cases.

What is the primary focus of the introduction to concurrency?

• Comparison of different programming paradigms

• Overview and importance of concurrency

• Detailed coding examples

• History of programming languages

What is a common characteristic of sequential programs?

• They require complex debugging

• They are always faster than concurrent programs

• They execute tasks one after another

• They utilize multiple CPU cores simultaneously

How can multiple CPU cores be utilized in concurrent programming?

• By running the same task on all cores

• By avoiding the use of threads

• By using only one core for all tasks

• By calculating independent tasks simultaneously on different cores

What is a key advantage of using threads for network-bound tasks?

• They simplify the program logic

• They eliminate the need for a database

• They allow tasks to proceed while waiting for network responses

• They increase the CPU usage

What is the Global Interpreter Lock (GIL) in Python?

• A tool for debugging concurrent programs

• A mechanism that allows multiple threads to run simultaneously

• A feature that restricts Python to run only one thread at a time

• A library for managing network requests

What is a race condition in concurrent programming?

• A situation where threads run faster than expected

• A method to optimize thread performance

• A condition where two threads access shared data simultaneously, leading to unpredictable results

• A type of error that occurs only in sequential programs

Why is thread safety important in concurrent programming?

• To ensure threads do not interfere with each other when accessing shared data

• To increase the speed of thread execution

• To reduce memory usage

• To simplify the program structure

What is a drawback of using threads in Python?

• They require more memory than processes

• They can only run on multiple CPU cores

• They are limited by the Global Interpreter Lock

• They cannot handle network-bound tasks

What is the difference between threading and multiprocessing in Python?

• Threading uses multiple cores, while multiprocessing uses a single core

• Threading is used for network tasks, while multiprocessing is not

• Threading shares memory, while multiprocessing has separate memory for each process

• Threading is faster than multiprocessing

Why might not every program need to be concurrent?

• Concurrency adds complexity and is not always necessary

• Concurrency always makes programs slower

• Concurrent programs cannot handle network tasks

• Sequential programs are always more efficient