👉 Preferred computing refers to the practice of optimizing and selecting specific computing resources, such as processors, memory, storage, and network bandwidth, based on the requirements of a particular application or workload. This approach aims to enhance performance, efficiency, and cost-effectiveness by tailoring the computing environment to match the specific needs of tasks, rather than using a one-size-fits-all approach. For example, a machine learning model might benefit from GPUs for parallel processing, while a data-intensive application could leverage SSDs for faster data access. Preferred computing involves analyzing factors like the type of computation, data size, and real-time requirements to make informed decisions about resource allocation, leading to better system performance and resource utilization.
