👉 Worker math, also known as distributed computing or parallel processing, is a method where computational tasks are divided among multiple workers or machines to speed up processing and solve complex problems more efficiently. Each worker performs a portion of the task, communicating with others as needed to share data and synchronize results. This approach leverages the combined power of many simpler, less powerful machines to tackle problems that would be too time-consuming or resource-intensive for a single machine. Common applications include big data analytics, scientific simulations, and machine learning, where tasks can be parallelized to achieve significant speedups.
