Grid computing in the context of Computer bus

A computer cluster is a set of computers that work together so that they can be viewed as a single system. Unlike grid computers, computer clusters have each node set to perform the same task, controlled and scheduled by software. The newest manifestation of cluster computing is cloud computing.

The components of a cluster are usually connected to each other through fast local area networks, with each node (computer used as a server) running its own instance of an operating system. In most circumstances, all of the nodes use the same hardware and the same operating system, although in some setups (e.g. using Open Source Cluster Application Resources (OSCAR)), different operating systems can be used on each computer, or different hardware.

In computer science, interprocess communication (IPC) is the sharing of data between running processes in a computer system, or between multiple such systems. Mechanisms for IPC may be provided by an operating system. Applications which use IPC are often categorized as clients and servers, where the client requests data and the server responds to client requests. Many applications are both clients and servers, as commonly seen in distributed computing.

IPC is very important to the design process for microkernels and nanokernels, which reduce the number of functionalities provided by the kernel. Those functionalities are then obtained by communicating with servers via IPC, leading to a large increase in communication when compared to a regular monolithic kernel. IPC interfaces generally encompass variable analytic framework structures. These processes ensure compatibility between the multi-vector protocols upon which IPC models rely.

E-Science or eScience is computationally intensive science that is carried out in highly distributed network environments, or science that uses immense data sets that require grid computing. The term sometimes includes technologies that enable distributed collaboration, such as the Access Grid. The term was created by John Taylor, the Director General of the United Kingdom's Office of Science and Technology in 1999 and was used to describe a large funding initiative starting in November 2000. E-science has been more broadly interpreted since then as "the application of computer technology to the undertaking of modern scientific investigation", including the preparation, experimentation, data collection, results dissemination, and long-term storage and accessibility of all materials generated through the scientific process. These may include data modeling and analysis, electronic/digitized laboratory notebooks, raw and fitted data sets, manuscript production and draft versions, pre-prints, and print and/or electronic publications." In 2014, IEEE eScience Conference Series condensed the definition to "eScience promotes innovation in collaborative, computationally- or data-intensive research across all disciplines, throughout the research lifecycle" in one of the working definitions used by the organizers. E-science encompasses "what is often referred to as big data [which] has revolutionized science... [such as] the Large Hadron Collider (LHC) at CERN... [that] generates around 780 terabytes per year... highly data intensive modern fields of science...that generate large amounts of E-science data include: computational biology, bioinformatics, genomics" and the human digital footprint for the social sciences.

Turing Award winner Jim Gray imagined "data-intensive science" or "e-science" as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.