Concurrent and Parallel Programming in Python - Multiprocessing Pool

The video tutorial explores the use of multiprocessing in Python, highlighting its similarities to threading. It discusses optimization strategies, particularly in handling network IO, and introduces the concept of a processing pool for simple tasks. The tutorial explains how to initialize and use the pool class, including the map method, and emphasizes adapting multiprocessing to the machine's capabilities using CPU count. The goal is to efficiently distribute work across available CPUs without extensive refactoring.

What is a key similarity between multiprocessing and threading?

• Both are used exclusively for network IO tasks.

• Both can utilize class inheritance and similar methods.

• Both require the use of queues for data handling.

• Both use the same class for implementation.

Why might you use queues in a threading program?

• To handle network IO efficiently.

• To simplify the code structure.

• To avoid using multiple threads.

• To increase the number of processes.

What is the first step in using a processing pool?

• Start with a simple function like squaring numbers.

• Initialize the pool with a specific number of processes.

• Use queues to manage data flow.

• Define a complex function to process data.

How do you initialize a pool class?

• By using the 'thread' keyword.

• By specifying the number of threads.

• By using the 'with' statement and context.

• By defining a complex function first.

What does the 'map' method do in a multiprocessing pool?

• It handles network IO tasks.

• It creates a new process for each task.

• It applies a function to each element in an iterable.

• It manages the pool size automatically.

What happens when all processes are removed from a pool?

• You must wait for a process to return to the pool.

• The pool stops functioning.

• The system crashes due to overload.

• The pool automatically increases in size.

How can you determine the number of CPUs available on a machine?

• By using the 'CPU count' function.

• By checking the system settings manually.

• By initializing a pool with a large number.

• By using the 'thread count' function.

Why might you choose to use one less CPU than available?

• To maximize the use of all available resources.

• To simplify the code structure.

• To ensure the system has resources for other processes.

• To avoid overloading the network.

What is a benefit of using a multiprocessing pool?

• It allows for sequential processing of tasks.

• It simplifies the use of threading.

• It speeds up code execution by parallel processing.

• It reduces the need for complex functions.

How does multiprocessing improve code efficiency?

• By reducing the number of lines of code.

• By eliminating the need for threading.

• By using fewer system resources.

• By allowing tasks to run in parallel.