👉 Canal computing, also known as parallel processing via pipelines, is a method of processing large amounts of data in a highly efficient manner by breaking down complex tasks into smaller, sequential stages that can be executed concurrently. This approach is inspired by the operation of canals, where water flows through a series of connected channels to transport goods or materials from one point to another. In canal computing, data flows through a series of interconnected processing units or stages, each performing a specific computation or transformation. This setup allows for significant speed-ups in data processing, as each stage operates on the output of the previous one, minimizing idle time and maximizing throughput. The term "canal" metaphorically represents the pipeline-like structure, emphasizing the flow and sequential nature of data processing, making it particularly effective for tasks like scientific simulations, data analytics, and machine learning where large datasets need to be processed rapidly.
