Grid Computing
Some think about this to function as "the third it wave" after the Internet and Web, and will be the backbone of another generation of services and applications that are going to further the study and development of GIS and related areas.
Grid computing permits the sharing of processing power, enabling the attainment of high performances in computing, management and services. Grid computing, (unlike the traditional supercomputer that does parallel computing by linking multiple processors over something bus) runs on the network of computers to execute a program. The problem of using multiple computers lies in the issue of dividing up the tasks on the list of computers, without having to reference portions of the code being executed on other CPUs.
Parallel processing
Parallel processing is the usage of multiple CPU's to execute different parts of a program together. Remote sensing and surveying equipment have already been providing vast levels of spatial information, and how exactly to manage, process or dispose of this data have become major issues in neuro-scientific Geographic Information Science (GIS).
To solve these problems there has been much research in to the section of parallel processing of GIS information. This calls for 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 many different forms of distributed computing, two of the most frequent are clustering and grid processing.
The primary reasons for using parallel computing are:
Saves time.
Solve larger problems.
Provide concurrency (do multiple things simultaneously).
Taking Scan to BIM Badminton of 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 an increasing number of transistors to be positioned on a chip.
However, even with molecular or atomic-level components, a limit will be reached on what small components could be.
Economic limitations - it is increasingly expensive to create a single processor faster. Using a larger number of moderately fast commodity processors to achieve the same (or better) performance is less expensive.
The future: during 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 may be the future of computing.
Distributed GIS
As 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 together with 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 as a possible 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 contains the opportunity 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 make it possible for this distributed, parallel, and high-throughput, collaborative GIS application.
Security
Security issues in that wide area distributed GIS is critical, which include authentication and authorization using community policies together with allowing local control of resource. Grid Security Infrastructure (GSI), coupled with GridFTP protocol, makes sure that sharing and transfer of geospatial data and Geoprocessing are secure in the Computational Grid environment.
Conclusion
As the conclusion, Grid computing has the possiblity to lead GIS into a new "Grid-enabled GIS" age in terms of computing paradigm, resource sharing pattern and online collaboration.