What defines grid computing compared to cluster computing?

Grid computing is characterized by a looser coupling of processes across multiple, distributed systems that collaborate to work on shared tasks. This setup allows resources from various locations, often across the internet, to be harnessed for compute-intensive problems. In grid computing, different systems and nodes can contribute their resources—such as processing power, storage, or bandwidth—without being directly connected in a tightly integrated manner. This approach enables greater flexibility and scalability compared to cluster computing, which typically involves a tighter interconnection of a few systems working closely together as a single unit.

In contrast to cluster computing, which usually focuses on high performance and low-latency communication within a confined environment, grid computing operates over more extensive network infrastructures and can take advantage of idle processing power from various machines. This cooperation towards common tasks, despite being looser in coupling, allows grid computing to efficiently utilize heterogeneous resources across various geographies, making it suitable for large-scale scientific computations and data processing tasks.

This fundamental difference in how processes are organized and interact highlights the utility and application of grid computing in scenarios that require massive computational support without the constraints of traditional clusters.