When should I use parallel processing instead of distributed processing?

Can you explain when to use parallel processing versus distributed processing?

Sure! Parallel processing is best for tasks that can be divided into smaller, independent tasks running simultaneously on a single machine. Distributed processing, on the other hand, is ideal for tasks that require multiple machines to work together, especially when dealing with large datasets.

What are some examples of tasks suitable for each type?

For parallel processing, tasks like image processing or simulations are great examples. For distributed processing, think of big data analytics or cloud computing tasks that need to scale across multiple servers.

Are there any performance considerations I should keep in mind?

Absolutely! Parallel processing can be limited by the resources of a single machine, while distributed processing can introduce network latency. It's important to assess the task's requirements and the available infrastructure.

How do I decide which one to use for my project?

Evaluate the size of your data, the complexity of the tasks, and the available resources. If tasks can run independently and you have a powerful single machine, go for parallel. If you need to scale out, choose distributed.