What technique does a firm use when it processes large amounts of data quickly by executing tasks on multiple processors simultaneously?

The technique that allows a firm to process large amounts of data quickly by executing tasks on multiple processors simultaneously is parallel processing. This method involves dividing a task into smaller sub-tasks that can be processed concurrently across multiple processors or cores, significantly reducing the overall time required to complete the task.

Parallel processing is particularly effective when dealing with data-intensive applications such as scientific computations, data analysis, and image processing. By maximizing the use of available computational resources, firms can achieve higher efficiency and faster results.

Other approaches, such as grid computing, involve distributing tasks across a network of computers but may not necessarily execute them simultaneously in the way parallel processing does. Multithreading allows a single process to manage multiple threads of execution, while multiprocessing focuses on executing multiple processes at the same time, typically improving performance but not necessarily dealing with coordinated task execution as effectively as parallel processing.