Parallel programming and distributed programming are two different approaches to utilizing multiple computing resources to solve a computational problem.
Parallel programming is a technique where a single program is executed simultaneously on multiple processors or cores within a single machine or node. The program is divided into smaller parts, called threads or processes, which can be executed concurrently. Parallel programming is often used to improve the performance and speed of computations that can be easily broken down into smaller, independent tasks that can be executed simultaneously.
On the other hand, distributed programming is a technique where a program is executed across multiple machines or nodes that are connected by a network. The program is divided into smaller parts, called tasks or processes, which are executed on different machines or nodes. Distributed programming is often used to handle large-scale, data-intensive computations that cannot be handled by a single machine.
In summary, the main difference between parallel programming and distributed programming is the level of concurrency and the location of computation. Parallel programming is focused on concurrency within a single machine or node, while distributed programming is focused on concurrency across multiple machines or nodes.
