👉 Row computing, also known as distributed row-wise computation, is a parallel computing paradigm where large datasets are processed in parallel across multiple computing nodes, each handling a subset of rows from a larger dataset. This approach is particularly useful for tasks that involve iterative computations or operations that can be applied independently to each row, such as matrix multiplications, linear algebra operations, or even certain types of machine learning algorithms. In row computing, each node processes its assigned rows concurrently, communicating with other nodes only when necessary to exchange results or synchronize operations. This method significantly enhances computational efficiency and scalability, making it ideal for handling big data problems where traditional serial processing would be too slow or resource-intensive.
