Grid Computing
Some consider this to be the "the third information technology wave" following the Internet and Web, and you will be the backbone of another generation of services and applications that are going to further the research and development of GIS and related areas.
Grid computing allows for the sharing of processing power, enabling the attainment of high performances in computing, management and services. Grid computing, (unlike the conventional supercomputer that does parallel computing by linking multiple processors over a system bus) runs on the network of computers to execute a program. The issue of using multiple computers lies in the difficulty of dividing up the tasks among the computers, and never have to reference portions of the code being executed on other CPUs.
Parallel processing
Parallel processing is the use of multiple CPU's to execute different sections of a program together. Remote sensing and surveying equipment have been providing vast amounts of spatial information, and how to manage, process or dispose of this data have grown to be major issues in the field of Geographic Information Science (GIS).
To resolve these problems there's been much research in to the section of parallel processing of GIS information. This involves the utilization of a single computer with multiple processors or multiple computers which are connected over a network working on the same task. There are various types of distributed computing, two of the most frequent are clustering and grid processing.
The primary known reasons for using parallel computing are:
Saves time.
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Provide concurrency (do multiple things at the same time).
Benefiting from non-local resources - using available computing resources on a wide area network, and even the web when local computing resources are scarce.
Cost benefits - using multiple cheap computing resources instead of paying for time on a supercomputer.
Overcoming memory constraints - single computers have very finite memory resources. For large problems, using the memories of multiple computers may overcome this obstacle.
Limits to serial computing - both physical and practical reasons pose significant constraints to simply building ever faster serial computers.
Limits to miniaturization - processor technology is allowing a growing number of transistors to be positioned on a chip.
However, despite having molecular or atomic-level components, a limit will undoubtedly be reached on what small components can be.
Economic limitations - it really is increasingly expensive to generate a single processor faster. Utilizing a larger amount of moderately fast commodity processors to achieve the same (or better) performance is less expensive.
The future: in the past a decade, the trends indicated by ever faster networks, distributed systems, and multi-processor computer architectures (even at the desktop level) clearly show that parallelism is the future of computing.
Distributed GIS
Because the development of GIS sciences and technologies go further, increasingly amount of geospatial and non-spatial data are involved in GISs because of more diverse data sources and development of data collection technologies. GIS data are usually geographically and logically distributed and GIS functions and services do. Spatial analysis and Geocomputation are receiving more complex and computationally intensive. Sharing and collaboration among geographically dispersed users with various disciplines with various purposes are getting more necessary and common. A dynamic collaborative model " Middleware" is necessary for GIS application.
Computational Grid is introduced just as one solution for another generation of GIS. Basically, the Grid computing concept is intended make it possible for coordinate resource sharing and problem solving in dynamic, multi-organizational virtual organizations by linking computing resources with high-performance networks. Grid computing technology represents a new method of collaborative computing and problem solving in data intensive and computationally intensive environment and has the chance to satisfy all of the requirements of a distributed, high-performance and collaborative GIS. Some methodologies and Grid computing technologies as solutions of requirements and challenges are introduced to enable this distributed, parallel, and high-throughput, collaborative GIS application.
Security
Security issues in such a wide area distributed GIS is crucial, which includes authentication and authorization using community policies and also allowing local control of resource. Grid Security Infrastructure (GSI), combined with GridFTP protocol, makes certain that sharing and transfer of geospatial data and Geoprocessing are secure in the Computational Grid environment.
Conclusion
As the conclusion, Grid computing has the chance to lead GIS right into a new "Grid-enabled GIS" age regarding computing paradigm, resource sharing pattern and online collaboration.