Grid Computing is a distributed computing paradigm that aggregates resources from multiple locations to work on large-scale computational tasks. It enables the sharing of processing power, storage, and data across geographically dispersed networks, often involving heterogeneous systems. This collaborative approach provides a scalable and cost-effective solution for solving complex problems.

Key Characteristics:
1. Decentralization: Resources are distributed across multiple nodes rather than centralized in a single location.
2. Resource Sharing: Enables collaboration by pooling computing power, storage, and specialized hardware.
3. Scalability: Supports dynamic resource allocation to meet changing workloads.
4. Fault Tolerance: Includes mechanisms for recovering tasks when nodes fail.

Applications:
- Scientific Research: Used in projects like protein folding simulations or astronomical data analysis.
- Financial Modeling: Conducting complex simulations and risk assessments.
- Data-Intensive Applications: Processing large datasets in fields like genomics or climate modeling.

Benefits:
- Efficiency: Maximizes the use of underutilized resources.
- Cost Savings: Reduces the need for expensive supercomputers.
- Collaboration: Facilitates joint efforts across organizations or institutions.

Grid computing systems, such as Globus Toolkit and BOINC (used in projects like SETI@home), highlight its potential for addressing resource-intensive challenges through cooperative computation.