Python Concurrency and Parallelism

Python Concurrency and Parallelism - Important Points

A. Merge sort

B. Bubble sort

C. Quick sort

D. Insertion sort

A. It can be more difficult to share state between processes.

B. It can be less efficient than using threads.

C. It can be more difficult to synchronize tasks between processes.

D. It can be more difficult to detect and debug issues in multiprocessing code.

A. They can share state more easily.

B. They can be more efficient than using processes.

C. They can be easier to synchronize.

D. They can be easier to debug.

A. Using the multiprocessing module

B. Using the threading module

C. Using the concurrent.futures module

D. Using the asyncio module

A. To provide a simple way to create and manage threads and processes.

B. To provide a way to execute code concurrently using coroutines.

C. To provide a way to execute code concurrently using threads and processes.

D. To provide a way to manage and control concurrency in Python code.