The Future of Grid Computing for Distributed GIS

Many consider grid computing to be the third information technology wave. In fact, it may very well come to play an important role in the GIS sector. GIS, or geographic information system, technology is used to gather, store, manipulate, and analyze geographic data for a variety of applications.

This type of technology can accomplish very complex calculations and algorithms, though it may require the use of multiple computers or systems to do so – this is referred to as grid computing.

What is Grid Computing?

Grid computing is simply the sharing of processing power among several computers instead of using one supercomputer for a project. Grid computing may also be done by one computer with several processors to execute a complex program. This is referred to as parallel processing.

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The problem with this type of computing is that it can be difficult to efficiently distribute tasks among individual computers. To help solve this problem, multiple computers can be used to perform multiple small tasks at the same time. This is often accomplished through the use of multi-core processors.

What are the Benefits of Parallel Processing?

Parallel processing is particularly common in the field of GIS because it provides an efficient way to gather and process vast amounts of information. Multi-core processors can be used to gather, manage, and process of large sets of data.

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This type of processing has a number of benefits for the GIS field including the following:

  • It saves a great deal of time by tackling multiple problems or calculations at once. This process is commonly used for high-performance computing.
  • It is an efficient method for solving larger problems – rather than having one computer solve multiple tasks, multiple processors solve individual tasks or parts of individual tasks.
  • It enables the use of non-local resources (including the internet) when local resources are unavailable.
  • It saves money by using multiple inexpensive computing sources rather than one expensive supercomputer.
  • It is a good way to overcome memory constraints – using multiple computers overcomes the problem of a single computer having a finite memory resource.
  • Both physical and practical factors limit the ability to build ever-faster serial computing units.
  • Economic factors limit the ability to make serial processing units increasingly faster – it is less expensive to use a larger number of cheaper processors.
  • Advances in processing technology have increased the number of transistors that can be used on a chip but there are practical limits to how small those parts can be – this limits the number that can be used on each chip.

Conclusion

Within the past decade, many advances have been made in computer technology. These advances have resulted in reducing the size of processing components which, in turn, makes parallel processing less expensive and faster than normal processing.

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Multi-processors have also become more advanced, making their uses for the GIS field more versatile than ever before. Grid computing is closely linked to recent advances in GIS technologies. Not only does grid computing make resource sharing faster than ever, but it also makes problem solving faster and easier too. It is also much less expensive than traditional computing models.

One thing that needs to be thought about in regard to grid computing for GIS, however, is the issue of security. To ensure security in distributed GIS, there needs to be certain authentication and authorization policies in place as well as a grid security infrastructure (GSI).

 

References:
http://searchdatacenter.techtarget.com/definition/grid-computing
http://computer.howstuffworks.com/grid-computing.htm
http://www.gislounge.com/grid-computing-in-distributed-gis/

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