Grid Computing
- [The University of Chicago]
- Overview
Grid computing, also known as virtual supercomputing, is a system that combines computers from different locations to work together as a virtual supercomputer to perform large tasks.
The computers are loosely linked together by the internet or other networks, and each node uses its resources to run independent tasks that contribute to the larger goal. For example, unused resources on multiple computers are pooled together and made available for a single task.
Grid computing can be used for a variety of purposes, including:
- Scientific and economic big data analyses
- Computationally intensive simulations
- Research in the natural sciences and medicine
- Meteorology
- The industrial sector
- Particle physics
Grid computing has been around since the beginning of the enterprise computing era, and originated in the academic community to process large data sets, such as satellite data, genomics, and nuclear physics.
- Supercomputing vs. Grid Computing
Grid computing differs from supercomputing in several important ways. For example, supercomputing can use parallel processing, which is when multiple CPUs work on solving a single calculation at a given time.
Here are some differences between supercomputing and grid computing:
- Supercomputing: Uses massive CPU resources and high-speed networking for complex data processing at scale. Supercomputers often consist of hundreds or thousands of nodes working in parallel.
- Grid computing: Uses multiple computers, often geographically distributed, to work together to accomplish joint tasks. Grid computing is ideal for big data analytics, as it allows for the parallel processing of large volumes of data.
- Grid Supercomputing
Grids are a form of distributed computing whereby a "super virtual computer" is composed of many networked loosely coupled computers acting together to perform large tasks, such as analyzing huge sets of data or weather modeling.
Through the cloud, you can assemble and use vast computer grids for specific time periods and purposes, paying, if necessary, only for what you use to save both the time and expense of purchasing and deploying the necessary resources yourself. Also by splitting tasks over multiple machines, processing time is significantly reduced to increase efficiency and minimize wasted resources.
Unlike with parallel computing, grid computing projects typically have no time dependency associated with them. They use computers that are part of the grid only when idle, and operators can perform tasks unrelated to the grid at any time.
Security must be considered when using computer grids as controls on member nodes are usually very loose. Redundancy should also be built in as many computers may disconnect or fail during processing.
The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files. Grid computing is distinguished from conventional high-performance computing systems such as cluster computing in that grid computers have each node set to perform a different task/application.
Grid computers also tend to be more heterogeneous and geographically dispersed (thus not physically coupled) than cluster computers. Although a single grid can be dedicated to a particular application, commonly a grid is used for a variety of purposes. Grids are often constructed with general-purpose grid middleware software libraries. Grid sizes can be quite large.
[More to come ...]
