👉 Arrange computing, also known as data flow or data-centric computing, is a paradigm where the primary focus is on the movement and processing of data rather than traditional computation involving instructions and registers. In this model, data flows through specialized processing units designed to handle specific tasks, such as matrix operations or signal processing, in a highly parallel and distributed manner. This approach leverages modern hardware architectures like GPUs, TPUs, and FPGAs to efficiently manage large datasets and complex computations. Unlike conventional computing, which emphasizes instruction execution on a central processing unit (CPU), arrange computing optimizes for data locality and throughput, making it particularly effective for machine learning, scientific simulations, and big data analytics. By aligning computational resources with the nature of the data, arrange computing can significantly enhance performance and energy efficiency in various applications.
