👉 Overhead computing refers to the additional computational resources, time, and costs incurred beyond the primary task or problem being solved. This overhead can arise from various sources such as managing communication between different parts of a distributed system, synchronizing processes, handling errors and exceptions, and maintaining data consistency. For instance, in a distributed computing environment, the overhead might involve transferring large amounts of data across network nodes, coordinating tasks among multiple machines, and dealing with potential failures or delays. In machine learning, overhead can include the computational cost of training models on large datasets, the need for extensive hyperparameter tuning, and the resource-intensive nature of parallel processing. While necessary for many complex operations, overhead computing can significantly impact the efficiency and scalability of computational tasks, making it a critical consideration in algorithm design and system architecture.
