Computational Grid


A Computational Grid refers to a hardware and software infrastructure that provides dependable, consistent, and pervasive access to high-end computational capabilities. It’s a type of distributed system that enables the sharing and coordinated use of diverse resources in dynamic, multi-institutional virtual organizations. The key goal is to facilitate resource sharing and collaborative problem-solving in science, engineering, and commerce.


The phonetics of the keyword “Computational Grid” is:- Computational: /ˌkɒmpjʊˈteɪʃənəl/- Grid: /ɡrɪd/

Key Takeaways

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  1. Resource Sharing: Computational grid is all about sharing and managing resources distributed across different locations. It allows for the efficient use of varied resources including processing power, data storage capacity, and software applications within a network.
  2. Performance Enhancement: One of the key benefits of computational grid is the significant increase in performance it brings by leveraging the combined computing power of the network. It is particularly beneficial for conducting complex computations or processing large volumes of data.
  3. Improved Collaboration: Through computational grid, organizations and individuals can collaborate more effectively, working together without being limited by geographical boundaries or the capacities of individual computers. It enables joint problem-solving and fosters innovation.


The term “Computational Grid” is significant in technology because it refers to a computing model that provides the ability to perform high-level computing more extensively and on a much larger scale than on a single computer. It uses the collective power of multiple resources scattered across diverse locations interconnected through a network to solve complex computational problems. This results in enhanced computational power, data storage capacity, and integrated resources optimized for dynamic load balancing, resource sharing, and parallel processing. It’s particularly important in fields that require processing of voluminous data such as scientific research, data analysis, remote sensing, and bioinformatics, making them more efficient and comprehensive.


The primary purpose of a computational grid is to leverage the power of multiple computers to process large sets of data and complex calculations over a decentralized network. In essence, this enables the sharing and managing of vast resources across geographically dispersed systems for solving large scale computational problems. Often referred to as “distributed computing,” a computational grid works by breaking down a complicated computation into smaller tasks, allocating those tasks to various devices on the grid, and then reassembling the results. This is particularly useful for operations that are too demanding for a single system or would take an impractical amount of time to process.Computational grids are used in various fields that require significant computing power, including bioinformatics, physics, and climate modeling where massive amounts of data need to be processed quickly and accurately. The concept also finds application in commercial sectors, such as financial modeling and digital media creation. Furthermore, it’s a beneficial technology for research institutions that cannot afford the massive computing resources required for complex simulations or data analysis. By sharing resources in a computational grid, the financial burdens are significantly reduced while providing a powerful platform for computation and data processing.


Computational grid refers to a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational resources. Here are three real world examples:1. CERN (European Organization for Nuclear Research) grid: Distributed across the globe, this computational grid, known as LHC (Large Hadron Collider) Computing Grid, is used for the data processing and analysis performed for the experiments at CERN’s LHC. The grid comprises more than 170 computing centers in over 40 countries, merging and utilising the power of thousands of computers worldwide.2. TeraGrid: Funded by the National Science Foundation (NSF), TeraGrid is a USA based commodity-based computational grid. Through high-performance network connections, TeraGrid integrates high-performance computers, data resources and tools, and experimental facilities from across the country. Users of this grid can utilise these resources for various research applications such as bioinformatics, quantum mechanics, or climate modeling.3. The UK National Grid Service: This provides a reliable computational grid for the UK’s research community. The infrastructure is used for major scientific projects, like the simulation of protein folding in biology or the modelling of climate change. The service provides computing resources from a series of connected nodes across the country.

Frequently Asked Questions(FAQ)

**Q1: What is a Computational Grid?**A1: A Computational Grid is a type of computing technology that uses multiple computers connected over a network to solve complex computational problems. These are distributed systems that allow for the sharing and coordinated use of diverse resources in dynamic, multi-institutional virtual organizations.**Q2: How does Computational Grid differ from traditional computing systems?**A2: Traditional computing systems carry out computations on a single system. In contrast, Computational Grids leverage resources from multiple systems across a network, allowing for the processing of larger sets of data and more complex computations than a single machine could carry out.**Q3: What are the applications of Computational Grid?**A3: Computational Grids are commonly used in projects that require intense computational power. Examples include scientific research, financial modeling, climate modeling, oil exploration, and even animation production.**Q4: What are the main components of a Computational Grid?**A4: A Computational Grid primarily consists of computing resources that are widely dispersed geographically and functionally. It also includes various software, storage components, and an efficient and reliable network to connect them all.**Q5: How does a Computational Grid work?**A5: Problems are broken down into smaller tasks, each of which is sent to a computer within the grid to be processed. The results are then combined to solve the original problem. This process is managed through advanced scheduling and resource management software.**Q6: What are the advantages of using a Computational Grid?**A6: Computational Grids provide the ability to harness the power of multiple computers, resulting in faster computational speed and increased capacity for data processing. They are scalable, cost-effective, and can provide a more reliable computing environment due to their distributed nature.**Q7: Are there any potential downsides to a Computational Grid?**A7: While they are powerful tools, Computational Grids can require significant resources and administrative effort to set up and manage. Ensuring data security and effective resource allocation can also be challenging in a distributed system. They may not be suitable for all types of computations, especially those that need real-time response.

Related Finance Terms

  • Distributed Computing
  • Grid Middleware
  • Resource Allocation
  • High-throughput Computing
  • Parallel Processing

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