Final answer:
The key difference is that grid computing involves a network of loosely connected computers working on large-scale tasks, whereas multiprocessing happens within a single system with multiple processors working simultaneously. Parallel processing encompasses both but is a broader concept.
Step-by-step explanation:
A key difference between grid computing, multiprocessing, and parallel processing is their architectural design, the scale of their deployment, and their problem-solving approach. Grid computing refers to a distributed system that consists of a network of loosely connected computers working on a task. These computers could be geographically dispersed and the tasks are often large-scale such as scientific computations or analyzing large data sets. Multiprocessing, by contrast, typically occurs within a single system or machine that has multiple processors (or cores) working independently or collaboratively on different processes simultaneously. Parallel processing can be seen as the overarching concept that includes any computing architecture where multiple processors execute multiple tasks at the same time, with multiprocessing being a subset of this.
In summary, while both grid computing and multiprocessing fall under the broad category of parallel processing, grid computing is distinct in its distributed nature across multiple systems, while multiprocessing refers to multiple CPUs or cores within a single system. The choice between grid computing and multiprocessing depends on the specific tasks, resource availability, and performance requirements of the work to be done.