Grid Computing
Some consider this to be the "the third information technology wave" following the Internet and Web, and 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 traditional supercomputer that does parallel computing by linking multiple processors over a system bus) uses a network of computers to execute an application. The problem of using multiple computers is based on the difficulty of dividing up the tasks among the computers, without having to reference portions of the code being executed on other CPUs.
Parallel processing
Parallel processing may be the usage 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 exactly to manage, process or dispose of this data have become major issues in neuro-scientific Geographic Information Science (GIS).
To solve https://buildinginformationmodelling.uk/best-scan-to-bim-gloucestershire/ there's been much research into 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 types of distributed computing, two of the most common are clustering and grid processing.
The primary known reasons for using parallel computing are:
Saves time.
Solve larger problems.
Provide concurrency (do multiple things at the same time).
Benefiting from non-local resources - using available computing resources on a broad area network, or even the Internet when local computing resources are scarce.
Cost benefits - using multiple cheap computing resources rather than 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 placed on a chip.
However, even with molecular or atomic-level components, a limit will be reached on how small components could be.
Economic limitations - it really is increasingly expensive to produce a single processor faster. Using a larger amount of moderately fast commodity processors to attain the same (or better) performance is less costly.
The future: in 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 quantity of geospatial and non-spatial data are involved in GISs due to more diverse data sources and development of data collection technologies. GIS data are usually geographically and logically distributed and also GIS functions and services do. Spatial analysis and Geocomputation are getting more technical and computationally intensive. Sharing and collaboration among geographically dispersed users with various disciplines with various purposes are receiving 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 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 approach to 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 to enable this distributed, parallel, and high-throughput, collaborative GIS application.
Security
Security issues in such a wide area distributed GIS is crucial, which include authentication and authorization using community policies along 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
Because the conclusion, Grid computing has the possiblity to lead GIS into a new "Grid-enabled GIS" age with regard to computing paradigm, resource sharing pattern and online collaboration.