👉 Cap computing, also known as Capacity Computing or Capacity Computing, is a computational paradigm that focuses on efficiently managing and utilizing large-scale data resources, particularly in distributed environments. It involves the orchestration of heterogeneous computing resources—such as CPUs, GPUs, FPGAs, and specialized accelerators—to process vast amounts of data in parallel, optimizing for both performance and energy efficiency. Cap computing systems are designed to dynamically allocate resources based on the specific needs of tasks, ensuring that data is processed where and when it's most effective. This approach is crucial in big data analytics, machine learning, and other applications requiring high throughput and low latency. By abstracting the complexities of resource management, cap computing enables developers to focus on algorithm design and data processing, rather than the underlying infrastructure, leading to more scalable and efficient computing solutions.
