The term computing is synonymous with counting and calculating. Originally, people that performed these functions were known as computers. Today it refers to a science and technology that deals with the computation and the manipulation of symbols. "Computing" also refers to the operation and usage of computing machines, the electrical processes carried out within the computing hardware itself, and the theoretical concepts governing them (computer science).
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The term computing has sometimes been narrowly defined, as in a 1989 ACM report on Computing as a Discipline[1]:
The discipline of computing is the systematic study of algorithmic processes that describe and transform information: their theory, analysis, design, efficiency, implementation, and application. The fundamental question underlying all computing is 'What can be (efficiently) automated?'
However, a broader definition is generally accepted, as illustrated by the 2005 joint report of the ACM and the IEEE, Computing Curricula 2005[2]:
In a general way, we can define computing to mean any goal-oriented activity requiring, benefiting from, or creating computers. Thus, computing includes designing and building hardware and software systems for a wide range of purposes; processing, structuring, and managing various kinds of information; doing scientific studies using computers; making computer systems behave intelligently; creating and using communications and entertainment media; finding and gathering information relevant to any particular purpose, and so on. The list is virtually endless, and the possibilities are vast.
The same report also recognizes that the meaning of computing depends on the context:
Computing also has other meanings that are more specific, based on the context in which the term is used. For example, an information systems specialist will view computing somewhat differently from a software engineer. Regardless of the context, doing computing well can be complicated and difficult. Because society needs people to do computing well, we must think of computing not only as a profession but also as a discipline.
In short, the concept of computing relates to human knowledge and activities which develop and use computer technologies.
Metacomputing, as a computing of computing, includes: organization of large computer networks , choice of the design criteria (for example: peer-to-peer or centralized solution) and metacomputing software (middleware, metaprogramming) development where, in the specific domains, the concept metacomputing is used as a description of software meta-layers which are networked platforms for the development of user-oriented calculations, for example for computational physics and bio-informatics.
Here, serious scientific problems of systems/networks complexity emerge , not only related to domain-dependent complexities but focused on systemic meta-complexity of computer network infrastructures.
From the human perspective and social perspective, metacomputing is especially focused on: human-computer software, cognitive interrelations/interfaces, the possibilities of the development of intelligent computer grides for the cooperation of human organizations, as well as, on ubiquitous computing technologies. In particular, tt relates to the development of software infrastructures for the computational modeling and simulation of cognitive architectures for various decision support systems.
Metacomputing refers to the general problems of computationality
of the human knowledge, to the limits of the transformation of human
knowledge and individual thinking to the form of computer programs.
These and similar questions are also of interest of mathematical psychology.
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A model of computation is a term from the theory of computations: computability theory and computational complexity theory.
A model of computation is the definition of the set of allowable operations used in computation and their respective costs. Only assuming a certain model of computation it is possible to analyze the computational resources required, such as the execution time or memory space or to discuss the limitations of algorithms or computers.
In discussing asymptotic estimates of computational complexity, it is common to specify a computational model in terms of primitive operations allowed which have unit cost, or simply unit-cost operations.
There are many models of computation, differing in the set of admissible operations and their computations cost. They fall into the following broad categories: abstract machine (abstract computer), used in proofs of computability and upper bounds on computational complexity of algorithms, and decision tree models, used in proofs of lower bounds on computational complexity of algorithmic problems.
Computational model is a mathematical model in computational science that requires extensive computational resources to study the behavior of a complex system by computer simulation. The system under study is often a complex nonlinear system that simple, intuitive analytical solutions are not readily available. Rather than deriving a mathematical analytical solution to the problem, experimentation with the model is done by changing the parameters of the system in the computer, and study the differences in the outcome of the experiments. Theories of operation of the model can be derived/deduced from these computational experiments.
Examples of common computational models are weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, and neural network models.