Grid Computing
Some think about this to function as "the third it wave" following 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 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 something bus) runs on the network of computers to execute an application. The issue of using multiple computers is based on the difficulty of dividing up the tasks on the list of computers, without needing to reference portions of the code being executed on other CPUs.
Parallel processing
Parallel processing may be the use of multiple CPU's to execute different parts of an application together. Remote sensing and surveying equipment have already been providing vast amounts of spatial information, and how to manage, process or get rid of this data have become 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. https://buildinginformationmodelling.uk/best-scan-to-bim-somerset/ calls for the utilization of a single computer with multiple processors or multiple computers that are connected over a network focusing on the same task. There are various forms of distributed computing, two of the most common are clustering and grid processing.
The primary reasons for using parallel computing are:
Saves time.
Solve larger problems.
Provide concurrency (do multiple things as well).
Taking advantage of non-local resources - using available computing resources on a broad area network, as well as the Internet when local computing resources are scarce.
Cost savings - using multiple cheap computing resources instead of spending money on 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 undoubtedly be reached on what small components can be.
Economic limitations - it is increasingly expensive to make 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: during the past 10 years, 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
Because the development of GIS sciences and technologies go further, increasingly level of geospatial and non-spatial data get excited about GISs due to more diverse data sources and development of data collection technologies. GIS data are usually geographically and logically distributed as well as GIS functions and services do. Spatial analysis and Geocomputation are receiving more technical 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 the next generation of GIS. Basically, the Grid computing concept is supposed to enable 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 opportunity to satisfy all 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 along with 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
Because the conclusion, Grid computing gets the chance to lead GIS into a new "Grid-enabled GIS" age when it comes to computing paradigm, resource sharing pattern and online collaboration.