What is the difference between parallel processing and distributed processing?

What is the difference between parallel processing and distributed processing?

Parallel processing involves dividing a task into smaller sub-tasks that are processed simultaneously on multiple processors. Distributed processing, on the other hand, involves multiple computers working together to complete a task, often over a network.

Can you give me an example of each?

Sure! An example of parallel processing is a multi-core CPU executing different parts of a program at the same time. An example of distributed processing is a cloud computing system where different servers handle different parts of a large data analysis task.

Which one is more efficient?

It depends on the task. Parallel processing is often more efficient for tasks that can be easily divided, while distributed processing is better for tasks that require large-scale resources or are geographically dispersed.

What are the challenges of each?

Challenges of parallel processing include managing data dependencies and synchronization. For distributed processing, challenges include network latency, data consistency, and fault tolerance.