1.
Computer
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A computer is a device that can be instructed to carry out an arbitrary set of arithmetic or logical operations automatically. The ability of computers to follow a sequence of operations, called a program, such computers are used as control systems for a very wide variety of industrial and consumer devices. The Internet is run on computers and it millions of other computers. Since ancient times, simple manual devices like the abacus aided people in doing calculations, early in the Industrial Revolution, some mechanical devices were built to automate long tedious tasks, such as guiding patterns for looms. More sophisticated electrical machines did specialized analog calculations in the early 20th century, the first digital electronic calculating machines were developed during World War II. The speed, power, and versatility of computers has increased continuously and dramatically since then, conventionally, a modern computer consists of at least one processing element, typically a central processing unit, and some form of memory. The processing element carries out arithmetic and logical operations, and a sequencing, peripheral devices include input devices, output devices, and input/output devices that perform both functions. Peripheral devices allow information to be retrieved from an external source and this usage of the term referred to a person who carried out calculations or computations. The word continued with the same meaning until the middle of the 20th century, from the end of the 19th century the word began to take on its more familiar meaning, a machine that carries out computations. The Online Etymology Dictionary gives the first attested use of computer in the 1640s, one who calculates, the Online Etymology Dictionary states that the use of the term to mean calculating machine is from 1897. The Online Etymology Dictionary indicates that the use of the term. 1945 under this name, theoretical from 1937, as Turing machine, devices have been used to aid computation for thousands of years, mostly using one-to-one correspondence with fingers. The earliest counting device was probably a form of tally stick, later record keeping aids throughout the Fertile Crescent included calculi which represented counts of items, probably livestock or grains, sealed in hollow unbaked clay containers. The use of counting rods is one example, the abacus was initially used for arithmetic tasks. The Roman abacus was developed from used in Babylonia as early as 2400 BC. Since then, many forms of reckoning boards or tables have been invented. In a medieval European counting house, a checkered cloth would be placed on a table, the Antikythera mechanism is believed to be the earliest mechanical analog computer, according to Derek J. de Solla Price. It was designed to calculate astronomical positions and it was discovered in 1901 in the Antikythera wreck off the Greek island of Antikythera, between Kythera and Crete, and has been dated to circa 100 BC
2.
Automata theory
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Automata theory is the study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in computer science and discrete mathematics. The word automata comes from the Greek word αὐτόματα, which means self-acting, the figure at right illustrates a finite-state machine, which belongs to a well-known type of automaton. This automaton consists of states and transitions, as the automaton sees a symbol of input, it makes a transition to another state, according to its transition function, which takes the current state and the recent symbol as its inputs. Automata theory is related to formal language theory. An automaton is a representation of a formal language that may be an infinite set. Automata play a role in theory of computation, compiler construction, artificial intelligence, parsing. Following is a definition of one type of automaton, which attempts to help one grasp the essential concepts involved in automata theory/theories. An automaton is supposed to run on some given sequence of inputs in time steps. An automaton gets one input every step that is picked up from a set of symbols or letters. At any time, the symbols so far fed to the automaton as input, form a sequence of symbols. An automaton contains a set of states. At each instance in time of run, the automaton is in one of its states. At each time step when the automaton reads a symbol, it jumps or transitions to state that is decided by a function that takes the current state. This function is called the transition function, the automaton reads the symbols of the input word one after another and transitions from state to state according to the transition function, until the word is read completely. Once the input word has been read, the automaton is said to have stopped, depending on the final state, its said that the automaton either accepts or rejects an input word. There is a subset of states of the automaton, which is defined as the set of accepting states, if the final state is an accepting state, then the automaton accepts the word. The set of all the words accepted by an automaton is called the language of that automaton, any subset of the language of an automaton is a language recognized by that automaton
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Computer science
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Computer science is the study of the theory, experimentation, and engineering that form the basis for the design and use of computers. An alternate, more succinct definition of science is the study of automating algorithmic processes that scale. A computer scientist specializes in the theory of computation and the design of computational systems and its fields can be divided into a variety of theoretical and practical disciplines. Some fields, such as computational complexity theory, are highly abstract, other fields still focus on challenges in implementing computation. Human–computer interaction considers the challenges in making computers and computations useful, usable, the earliest foundations of what would become computer science predate the invention of the modern digital computer. Machines for calculating fixed numerical tasks such as the abacus have existed since antiquity, further, algorithms for performing computations have existed since antiquity, even before the development of sophisticated computing equipment. Wilhelm Schickard designed and constructed the first working mechanical calculator in 1623, in 1673, Gottfried Leibniz demonstrated a digital mechanical calculator, called the Stepped Reckoner. He may be considered the first computer scientist and information theorist, for, among other reasons and he started developing this machine in 1834, and in less than two years, he had sketched out many of the salient features of the modern computer. A crucial step was the adoption of a card system derived from the Jacquard loom making it infinitely programmable. Around 1885, Herman Hollerith invented the tabulator, which used punched cards to process statistical information, when the machine was finished, some hailed it as Babbages dream come true. During the 1940s, as new and more powerful computing machines were developed, as it became clear that computers could be used for more than just mathematical calculations, the field of computer science broadened to study computation in general. Computer science began to be established as an academic discipline in the 1950s. The worlds first computer science program, the Cambridge Diploma in Computer Science. The first computer science program in the United States was formed at Purdue University in 1962. Since practical computers became available, many applications of computing have become distinct areas of study in their own rights and it is the now well-known IBM brand that formed part of the computer science revolution during this time. IBM released the IBM704 and later the IBM709 computers, still, working with the IBM was frustrating if you had misplaced as much as one letter in one instruction, the program would crash, and you would have to start the whole process over again. During the late 1950s, the science discipline was very much in its developmental stages. Time has seen significant improvements in the usability and effectiveness of computing technology, modern society has seen a significant shift in the users of computer technology, from usage only by experts and professionals, to a near-ubiquitous user base
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Computer engineering
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Computer engineering is a discipline that integrates several fields of electrical engineering and computer science required to develop computer hardware and software. Computer engineers usually have training in engineering, software design. This field of engineering not only focuses on how computer systems themselves work, Computer engineers are also suited for robotics research, which relies heavily on using digital systems to control and monitor electrical systems like motors, communications, and sensors. Other institutions may require engineering students to one or two years of General Engineering before declaring computer engineering as their primary focus. The first computer engineering program in the United States was established at Case Western Reserve University in 1972. As of 2015, there were 238 ABET-accredited computer engineering programs in the US, in Europe, accreditation of computer engineering schools is done by a variety of agencies part of the EQANIE network. Both computer engineering and electronic engineering programs include analog and digital circuit design in their curriculum, as with most engineering disciplines, having a sound knowledge of mathematics and science is necessary for computer engineers. There are two major specialties in computer engineering, software and hardware, Computer software engineers develop, design, and test software. They construct, and maintain computer programs, as well as set up such as intranets for companies. Software engineers can design or code new applications to meet the needs of a business or individual. Some software engineers work independently as freelancers and sell their software products/applications to an enterprise or individual, most computer hardware engineers research, develop, design, and test various computer equipment. This can range from circuit boards and microprocessors to routers, some update existing computer equipment to be more efficient and work with newer software. Most computer hardware engineers work in laboratories and high-tech manufacturing firms. Some also work for the federal government, according to BLS, 95% of computer hardware engineers work in metropolitan areas. Approximately 33% of their work more than 40 hours a week. The median salary for employed qualified computer hardware engineers was $100,920 per year or $48.52 per hour, Computer hardware engineers held 83,300 jobs in 2012 in the USA. There are many specialty areas in the field of computer engineering, examples include work on wireless communications, multi-antenna systems, optical transmission, and digital watermarking. Those focusing on communications and wireless networks, work advancements in telecommunications systems and networks, modulation and error-control coding, high-speed network design, interference suppression and modulation, design and analysis of fault-tolerant system, and storage and transmission schemes are all a part of this specialty
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Paradigm
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Paradigm comes from Greek παράδειγμα, pattern, example, sample from the verb παραδείκνυμι, exhibit, represent, expose and that from παρά, beside, beyond and δείκνυμι, to show, to point out. In rhetoric, paradeigma is known as a type of proof, the purpose of paradeigma is to provide an audience with an illustration of similar occurrences. This illustration is not meant to take the audience to a conclusion, a personal accountant is a good comparison of paradeigma to explain how it is meant to guide the audience. Anaximenes defined paradeigma as, actions that have occurred previously and are similar to, or the opposite of, the original Greek term παράδειγμα was used in Greek texts such as Platos Timaeus as the model or the pattern that the Demiurge used to create the cosmos. In linguistics, Ferdinand de Saussure used paradigm to refer to a class of elements with similarities, normal science proceeds within such a framework or paradigm. A paradigm does not impose a rigid or mechanical approach, the Oxford English Dictionary defines the basic meaning of the term paradigm as a typical example or pattern of something, a pattern or model. e. Firstly, within normal science, the term refers to the set of experiments that are likely to be copied or emulated. Secondly, underpinning this set of exemplars are shared preconceptions, made prior to – and these preconceptions embody both hidden assumptions and elements that he describes as quasi-metaphysical, the interpretations of the paradigm may vary among individual scientists. Kuhn was at pains to point out that the rationale for the choice of exemplars is a way of viewing reality, that view. For well-integrated members of a discipline, its paradigm is so convincing that it normally renders even the possibility of alternatives unconvincing. Such a paradigm is opaque, appearing to be a view of the bedrock of reality itself. The conviction that the current paradigm is reality tends to disqualify evidence that might undermine the paradigm itself and it is the latter that is responsible for the eventual revolutionary overthrow of the incumbent paradigm, and its replacement by a new one. Kuhn used the expression paradigm shift for this process, and likened it to the change that occurs when our interpretation of an ambiguous image flips over from one state to another. This is significant in relation to the issue of incommensurability, an example of a currently accepted paradigm would be the standard model of physics. Mechanisms similar to the original Kuhnian paradigm have been invoked in various disciplines other than the philosophy of science and these include, the idea of major cultural themes, worldviews, ideologies, and mindsets. They have somewhat similar meanings that apply to smaller and larger examples of disciplined thought. In addition, Michel Foucault used the terms episteme and discourse, mathesis and taxinomia, in The Structure of Scientific Revolutions, Kuhn wrote that the successive transition from one paradigm to another via revolution is the usual developmental pattern of mature science. New paradigms tend to be most dramatic in sciences that appear to be stable and mature, at that time, a statement generally attributed to physicist Lord Kelvin famously claimed, There is nothing new to be discovered in physics now
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Theory of computation
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In theoretical computer science and mathematics, the theory of computation is the branch that deals with how efficiently problems can be solved on a model of computation, using an algorithm. In order to perform a study of computation, computer scientists work with a mathematical abstraction of computers called a model of computation. There are several models in use, but the most commonly examined is the Turing machine and it might seem that the potentially infinite memory capacity is an unrealizable attribute, but any decidable problem solved by a Turing machine will always require only a finite amount of memory. So in principle, any problem that can be solved by a Turing machine can be solved by a computer that has an amount of memory. The theory of computation can be considered the creation of models of all kinds in the field of computer science, therefore, mathematics and logic are used. In the last century it became an independent academic discipline and was separated from mathematics, some pioneers of the theory of computation were Alonzo Church, Kurt Gödel, Alan Turing, Stephen Kleene, John von Neumann and Claude Shannon. Automata theory is the study of abstract machines and the problems that can be solved using these machines. These abstract machines are called automata, Automata comes from the Greek word which means that something is doing something by itself. Automata theory is closely related to formal language theory, as the automata are often classified by the class of formal languages they are able to recognize. An automaton can be a representation of a formal language that may be an infinite set. Automata are used as models for computing machines, and are used for proofs about computability. Language theory is a branch of mathematics concerned with describing languages as a set of operations over an alphabet and it is closely linked with automata theory, as automata are used to generate and recognize formal languages. Because automata are used as models for computation, formal languages are the mode of specification for any problem that must be computed. Computability theory deals primarily with the question of the extent to which a problem is solvable on a computer, much of computability theory builds on the halting problem result. Many mathematicians and computational theorists who study recursion theory will refer to it as computability theory, Complexity theory considers not only whether a problem can be solved at all on a computer, but also how efficiently the problem can be solved. In order to analyze how much time and space a given algorithm requires, for example, finding a particular number in a long list of numbers becomes harder as the list of numbers grows larger. If we say there are n numbers in the list, then if the list is not sorted or indexed in any way we may have to look at every number in order to find the number were seeking. We thus say that in order to solve this problem, the needs to perform a number of steps that grows linearly in the size of the problem
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Thought experiment
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A thought experiment considers some hypothesis, theory, or principle for the purpose of thinking through its consequences. Given the structure of the experiment, it may not be possible to perform it, perhaps the key experiment in the history of modern science is Galileos demonstration that falling objects must fall at the same rate regardless of their masses. The experiment is described by Galileo in Discorsi e dimostrazioni matematiche thus, do you not agree with me in this opinion. Hence the heavier body moves with less speed than the lighter, thus you see how, from your assumption that the heavier body moves more rapidly than the lighter one, I infer that the heavier body moves more slowly. Although the extract does not convey the elegance and power of the demonstration terribly well, it is clear that it is a thought experiment, instead, many philosophers prefer to consider Thought Experiments to be merely the use of a hypothetical scenario to help understand the way things are. Thought experiments have used in a variety of fields, including philosophy, law, physics. In philosophy, they have used at least since classical antiquity. In law, they were well-known to Roman lawyers quoted in the Digest, in physics and other sciences, notable thought experiments date from the 19th and especially the 20th century, but examples can be found at least as early as Galileo. Johann Witt-Hansen established that Hans Christian Ørsted was the first to use the Latin-German mixed term Gedankenexperiment circa 1812, Ørsted was also the first to use its entirely German equivalent, Gedankenversuch, in 1820. The English term thought experiment was coined from Machs Gedankenexperiment, prior to its emergence, the activity of posing hypothetical questions that employed subjunctive reasoning had existed for a very long time. However, people had no way of categorizing it or speaking about it and this helps to explain the extremely wide and diverse range of the application of the term thought experiment once it had been introduced into English. In physics and other sciences many thought experiments date from the 19th and especially the 20th Century, in Galileo’s thought experiment, for example, the rearrangement of empirical experience consists in the original idea of combining bodies of different weight. Thought experiments have used in philosophy, physics, and other fields. In law, the hypothetical is frequently used for such experiments. Regardless of their goal, all thought experiments display a patterned way of thinking that is designed to allow us to explain, predict and control events in a better. However, they may make those theories themselves irrelevant, and could create new problems that are just as difficult. Ensure the avoidance of past failures Scientists tend to use thought experiments as imaginary, in these cases, the result of the proxy experiment will often be so clear that there will be no need to conduct a physical experiment at all. Scientists also use thought experiments when particular physical experiments are impossible to conduct, such as Einsteins thought experiment of chasing a light beam, leading to Special Relativity
8.
Algorithm
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In mathematics and computer science, an algorithm is a self-contained sequence of actions to be performed. Algorithms can perform calculation, data processing and automated reasoning tasks, an algorithm is an effective method that can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function. The transition from one state to the next is not necessarily deterministic, some algorithms, known as randomized algorithms, giving a formal definition of algorithms, corresponding to the intuitive notion, remains a challenging problem. In English, it was first used in about 1230 and then by Chaucer in 1391, English adopted the French term, but it wasnt until the late 19th century that algorithm took on the meaning that it has in modern English. Another early use of the word is from 1240, in a manual titled Carmen de Algorismo composed by Alexandre de Villedieu and it begins thus, Haec algorismus ars praesens dicitur, in qua / Talibus Indorum fruimur bis quinque figuris. Which translates as, Algorism is the art by which at present we use those Indian figures, the poem is a few hundred lines long and summarizes the art of calculating with the new style of Indian dice, or Talibus Indorum, or Hindu numerals. An informal definition could be a set of rules that precisely defines a sequence of operations, which would include all computer programs, including programs that do not perform numeric calculations. Generally, a program is only an algorithm if it stops eventually, but humans can do something equally useful, in the case of certain enumerably infinite sets, They can give explicit instructions for determining the nth member of the set, for arbitrary finite n. An enumerably infinite set is one whose elements can be put into one-to-one correspondence with the integers, the concept of algorithm is also used to define the notion of decidability. That notion is central for explaining how formal systems come into being starting from a set of axioms. In logic, the time that an algorithm requires to complete cannot be measured, from such uncertainties, that characterize ongoing work, stems the unavailability of a definition of algorithm that suits both concrete and abstract usage of the term. Algorithms are essential to the way computers process data, thus, an algorithm can be considered to be any sequence of operations that can be simulated by a Turing-complete system. Although this may seem extreme, the arguments, in its favor are hard to refute. Gurevich. Turings informal argument in favor of his thesis justifies a stronger thesis, according to Savage, an algorithm is a computational process defined by a Turing machine. Typically, when an algorithm is associated with processing information, data can be read from a source, written to an output device. Stored data are regarded as part of the state of the entity performing the algorithm. In practice, the state is stored in one or more data structures, for some such computational process, the algorithm must be rigorously defined, specified in the way it applies in all possible circumstances that could arise. That is, any conditional steps must be dealt with, case-by-case
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Computational complexity theory
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A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. The theory formalizes this intuition, by introducing mathematical models of computation to study these problems and quantifying the amount of resources needed to solve them, such as time and storage. Other complexity measures are used, such as the amount of communication, the number of gates in a circuit. One of the roles of computational complexity theory is to determine the limits on what computers can. Closely related fields in computer science are analysis of algorithms. More precisely, computational complexity theory tries to classify problems that can or cannot be solved with appropriately restricted resources, a computational problem can be viewed as an infinite collection of instances together with a solution for every instance. The input string for a problem is referred to as a problem instance. In computational complexity theory, a problem refers to the question to be solved. In contrast, an instance of this problem is a rather concrete utterance, for example, consider the problem of primality testing. The instance is a number and the solution is yes if the number is prime, stated another way, the instance is a particular input to the problem, and the solution is the output corresponding to the given input. For this reason, complexity theory addresses computational problems and not particular problem instances, when considering computational problems, a problem instance is a string over an alphabet. Usually, the alphabet is taken to be the binary alphabet, as in a real-world computer, mathematical objects other than bitstrings must be suitably encoded. For example, integers can be represented in binary notation, and graphs can be encoded directly via their adjacency matrices and this can be achieved by ensuring that different representations can be transformed into each other efficiently. Decision problems are one of the objects of study in computational complexity theory. A decision problem is a type of computational problem whose answer is either yes or no. A decision problem can be viewed as a language, where the members of the language are instances whose output is yes. The objective is to decide, with the aid of an algorithm, if the algorithm deciding this problem returns the answer yes, the algorithm is said to accept the input string, otherwise it is said to reject the input. An example of a problem is the following
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Turing machine
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Despite the models simplicity, given any computer algorithm, a Turing machine can be constructed that is capable of simulating that algorithms logic. The machine operates on an infinite memory tape divided into discrete cells, the machine positions its head over a cell and reads the symbol there. The Turing machine was invented in 1936 by Alan Turing, who called it an a-machine, thus, Turing machines prove fundamental limitations on the power of mechanical computation. Turing completeness is the ability for a system of instructions to simulate a Turing machine, a Turing machine is a general example of a CPU that controls all data manipulation done by a computer, with the canonical machine using sequential memory to store data. More specifically, it is a capable of enumerating some arbitrary subset of valid strings of an alphabet. Assuming a black box, the Turing machine cannot know whether it will eventually enumerate any one specific string of the subset with a given program and this is due to the fact that the halting problem is unsolvable, which has major implications for the theoretical limits of computing. The Turing machine is capable of processing an unrestricted grammar, which implies that it is capable of robustly evaluating first-order logic in an infinite number of ways. This is famously demonstrated through lambda calculus, a Turing machine that is able to simulate any other Turing machine is called a universal Turing machine. The thesis states that Turing machines indeed capture the notion of effective methods in logic and mathematics. Studying their abstract properties yields many insights into computer science and complexity theory, at any moment there is one symbol in the machine, it is called the scanned symbol. The machine can alter the scanned symbol, and its behavior is in part determined by that symbol, however, the tape can be moved back and forth through the machine, this being one of the elementary operations of the machine. Any symbol on the tape may therefore eventually have an innings, the Turing machine mathematically models a machine that mechanically operates on a tape. On this tape are symbols, which the machine can read and write, one at a time, in the original article, Turing imagines not a mechanism, but a person whom he calls the computer, who executes these deterministic mechanical rules slavishly. If δ is not defined on the current state and the current tape symbol, Q0 ∈ Q is the initial state F ⊆ Q is the set of final or accepting states. The initial tape contents is said to be accepted by M if it eventually halts in a state from F, Anything that operates according to these specifications is a Turing machine. The 7-tuple for the 3-state busy beaver looks like this, Q = Γ = b =0 Σ = q 0 = A F = δ = see state-table below Initially all tape cells are marked with 0. In the words of van Emde Boas, p.6, The set-theoretical object provides only partial information on how the machine will behave and what its computations will look like. For instance, There will need to be many decisions on what the symbols actually look like, and a failproof way of reading and writing symbols indefinitely
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Instruction set architecture
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An ISA includes a specification of the set of opcodes, and the native commands implemented by a particular processor. An instruction set architecture is distinguished from a microarchitecture, which is the set of design techniques used, in a particular processor. Processors with different microarchitectures can share a common instruction set, for example, the Intel Pentium and the AMD Athlon implement nearly identical versions of the x86 instruction set, but have radically different internal designs. The concept of an architecture, distinct from the design of a machine, was developed by Fred Brooks at IBM during the design phase of System/360. Prior to NPL, the companys computer designers had been free to honor cost objectives not only by selecting technologies, the SPREAD compatibility objective, in contrast, postulated a single architecture for a series of five processors spanning a wide range of cost and performance. In addition, these virtual machines execute less frequently used code paths by interpretation, transmeta implemented the x86 instruction set atop VLIW processors in this fashion. A complex instruction set computer has many specialized instructions, some of which may only be used in practical programs. Theoretically important types are the instruction set computer and the one instruction set computer. Another variation is the very long instruction word where the processor receives many instructions encoded and retrieved in one instruction word, machine language is built up from discrete statements or instructions. Examples of operations common to many instruction sets include, Set a register to a constant value. Copy data from a location to a register, or vice versa. Used to store the contents of a register, result of a computation, often called load and store operations. Read and write data from hardware devices, add, subtract, multiply, or divide the values of two registers, placing the result in a register, possibly setting one or more condition codes in a status register. Increment, decrement in some ISAs, saving operand fetch in trivial cases, perform bitwise operations, e. g. taking the conjunction and disjunction of corresponding bits in a pair of registers, taking the negation of each bit in a register. Floating-point instructions for arithmetic on floating-point numbers, branch to another location in the program and execute instructions there. Conditionally branch to another if a certain condition holds. Call another block of code, while saving the location of the instruction as a point to return to. Load/store data to and from a coprocessor, or exchanging with CPU registers, processors may include complex instructions in their instruction set
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Memory hierarchy
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The memory hierarchy in computer storage separates each of its levels based on response time. Since response time, complexity, and capacity are related, the levels may also be distinguished by their performance, designing for high performance requires considering the restrictions of the memory hierarchy, i. e. the size and capabilities of each component. Each of the components can be viewed as part of a hierarchy of memories in which each member mi is typically smaller and faster than the next highest member mi+1 of the hierarchy. To limit waiting by higher levels, a level will respond by filling a buffer. There are four major storage levels, internal – Processor registers and cache. Main – the system RAM and controller cards, on-line mass storage – Secondary storage. Off-line bulk storage – Tertiary and Off-line storage and this is a general memory hierarchy structuring. Adding complexity slows down the memory hierarchy, latency and bandwidth are two metrics associated with caches and memory. Neither of them is uniform, but is specific to a component of the memory hierarchy. Predicting where in the hierarchy the data resides is difficult. the location in the memory hierarchy dictates the time required for the prefetch to occur. The number of levels in the hierarchy and the performance at each level has increased over time. For example, the hierarchy of an Intel Haswell Mobile processor circa 2013 is. A few thousand bytes in size Cache Level 0 Micro operations cache –6 KiB in size Level 1 Instruction cache –128 KiB in size Level 1 Data cache –128 KiB in size. Best access speed is around 700 GiB/second Level 2 Instruction and data –1 MiB in size, best access speed is around 200 GiB/second Level 3 Shared cache –6 MiB in size. Best access speed is around 100 GB/second Level 4 Shared cache –128 MiB in size, best access speed is around 40 GB/second Main memory – Gigabytes in size. Best access speed is around 10 GB/second, in the case of a NUMA machine, access times may not be uniform Disk storage – Terabytes in size. As of 2013, best access speed is from a solid state drive is about 600 MB/second Nearline storage – Up to exabytes in size. As of 2013, best access speed is about 160 MB/second Offline storage The lower levels of the hierarchy – from disks downwards – are also known as tiered storage, the formal distinction between online, nearline, and offline storage is, Online storage is immediately available for I/O
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Random access
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It is typically contrasted to sequential access. For example, data might be stored notionally in a sequence like a row. However, given all the coordinates, a program can access each record about as quickly and easily as any other, at first the term random access was used because the process had to be capable of finding records no matter in which sequence they were required. However, soon the term direct access gained favour because one could directly retrieve a record, the operative attribute however is that the device can access any required record immediately on demand. The opposite is sequential access, where a remote element takes longer time to access, a typical illustration of this distinction is to compare an ancient scroll and the book. A more modern example is a tape and a CD. In data structures, direct access implies the ability to access any entry in a list in constant time, very few data structures can guarantee this, other than arrays. Direct access is required, or at least valuable, in many such as binary search. Other data structures, such as linked lists, sacrifice direct access to permit efficient inserts, deletes, data stream Random-access machine Locality of reference
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Cache (computing)
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A cache hit occurs when the requested data can be found in a cache, while a cache miss occurs when it cannot. To be cost-effective and to enable efficient use of data, caches must be relatively small, nevertheless, caches have proven themselves in many areas of computing because access patterns in typical computer applications exhibit the locality of reference. There is an inherent trade-off between size and speed but also a tradeoff between expensive, premium technologies vs cheaper, easily mass-produced commodities and this is mitigated by reading in large chunks, in the hope that subsequent reads will be from nearby locations. Prediction or explicit prefetching might also guess where future reads will come from and make requests ahead of time, the use of a cache also allows for higher throughput from the underlying resource, by assembling multiple fine grain transfers into larger, more efficient requests. In the case of DRAM, this might be served by a wider bus, reading larger chunks reduces the fraction of bandwidth required for transmitting address information. Hardware implements cache as a block of memory for storage of data likely to be used again. Central processing units and hard disk drives use a cache, as do web browsers. A cache is made up of a pool of entries, each entry has associated data, which is a copy of the same data in some backing store. Each entry also has a tag, which specifies the identity of the data in the store of which the entry is a copy. When the cache client needs to access data presumed to exist in the backing store, if an entry can be found with a tag matching that of the desired data, the data in the entry is used instead. This situation is known as a cache hit, so, for example, a web browser program might check its local cache on disk to see if it has a local copy of the contents of a web page at a particular URL. In this example, the URL is the tag, and the contents of the web page is the data, the percentage of accesses that result in cache hits is known as the hit rate or hit ratio of the cache. The alternative situation, when the cache is consulted and found not to data with the desired tag, has become known as a cache miss. The previously uncached data fetched from the store during miss handling is usually copied into the cache. During a cache miss, the CPU usually ejects some other entry in order to make room for the previously uncached data, the heuristic used to select the entry to eject is known as the replacement policy. One popular replacement policy, least recently used, replaces the least recently used entry, more efficient caches compute use frequency against the size of the stored contents, as well as the latencies and throughputs for both the cache and the backing store. This works well for larger amounts of data, longer latencies and slower throughputs, such as experienced with a drive and the Internet. When a system writes data to cache, it must at some point write that data to the store as well
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Cache-oblivious algorithm
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In computing, a cache-oblivious algorithm is an algorithm designed to take advantage of a CPU cache without having the size of the cache as an explicit parameter. An optimal cache-oblivious algorithm is an algorithm that uses the cache optimally. Cache-oblivious algorithms are contrasted with explicit blocking, as in loop nest optimization, optimal cache-oblivious algorithms are known for the Cooley–Tukey FFT algorithm, matrix multiplication, sorting, matrix transposition, and several other problems. Because these algorithms are optimal in an asymptotic sense, further machine-specific tuning may be required to obtain nearly optimal performance in an absolute sense. The goal of cache-oblivious algorithms is to reduce the amount of tuning that is required. Typically, an algorithm works by a recursive divide and conquer algorithm. Eventually, one reaches a size that fits into cache. In tuning for a machine, one may use a hybrid algorithm which uses blocking tuned for the specific cache sizes at the bottom level. There were many predecessors, typically analyzing specific problems, these are discussed in detail in Frigo et al, early examples cited include Singleton 1969 for a recursive Fast Fourier Transform, similar ideas in Aggarwal et al. 1987, Frigo 1996 for matrix multiplication and LU decomposition, Cache-oblivious algorithms are typically analyzed using an idealized model of the cache, sometimes called the cache-oblivious model. This model is easier to analyze than a real caches characteristics. In particular, the model is an abstract machine. It is similar to the RAM machine model which replaces the Turing machines infinite tape with an infinite array, each location within the array can be accessed in O time, similar to the Random access memory on a real computer. Unlike the RAM machine model, it introduces a cache. The other differences between the two models are listed below, in the cache-oblivious model, Memory is broken into lines of L words each A load or a store between main memory and a CPU register may now be serviced from the cache. If a load or a store cannot be serviced from the cache, a cache miss results in one line being loaded from main memory into the cache. Namely, if the CPU tries to access word w and b is the line containing w, if the cache was previously full, then a line will be evicted as well. The cache holds Z words, where Z = Ω and this is also known as the tall cache assumption
16.
Microprocessor
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A microprocessor is a computer processor which incorporates the functions of a computers central processing unit on a single integrated circuit, or at most a few integrated circuits. Microprocessors contain both combinational logic and sequential digital logic, Microprocessors operate on numbers and symbols represented in the binary numeral system. The integration of a whole CPU onto a chip or on a few chips greatly reduced the cost of processing power. Integrated circuit processors are produced in numbers by highly automated processes resulting in a low per unit cost. Single-chip processors increase reliability as there are many electrical connections to fail. As microprocessor designs get better, the cost of manufacturing a chip generally stays the same, before microprocessors, small computers had been built using racks of circuit boards with many medium- and small-scale integrated circuits. Microprocessors combined this into one or a few large-scale ICs, the internal arrangement of a microprocessor varies depending on the age of the design and the intended purposes of the microprocessor. Advancing technology makes more complex and powerful chips feasible to manufacture, a minimal hypothetical microprocessor might only include an arithmetic logic unit and a control logic section. The ALU performs operations such as addition, subtraction, and operations such as AND or OR, each operation of the ALU sets one or more flags in a status register, which indicate the results of the last operation. The control logic retrieves instruction codes from memory and initiates the sequence of operations required for the ALU to carry out the instruction, a single operation code might affect many individual data paths, registers, and other elements of the processor. As integrated circuit technology advanced, it was feasible to manufacture more and more complex processors on a single chip, the size of data objects became larger, allowing more transistors on a chip allowed word sizes to increase from 4- and 8-bit words up to todays 64-bit words. Additional features were added to the architecture, more on-chip registers sped up programs. Floating-point arithmetic, for example, was not available on 8-bit microprocessors. Integration of the point unit first as a separate integrated circuit and then as part of the same microprocessor chip. Occasionally, physical limitations of integrated circuits made such practices as a bit slice approach necessary, instead of processing all of a long word on one integrated circuit, multiple circuits in parallel processed subsets of each data word. With the ability to put large numbers of transistors on one chip and this CPU cache has the advantage of faster access than off-chip memory, and increases the processing speed of the system for many applications. Processor clock frequency has increased more rapidly than external memory speed, except in the recent past, a microprocessor is a general purpose system. Several specialized processing devices have followed from the technology, A digital signal processor is specialized for signal processing, graphics processing units are processors designed primarily for realtime rendering of 3D images
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Interpreter (computing)
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An interpreter generally uses one of the following strategies for program execution, parse the source code and perform its behavior directly. Translate source code into some efficient intermediate representation and immediately execute this, explicitly execute stored precompiled code made by a compiler which is part of the interpreter system. Early versions of Lisp programming language and Dartmouth BASIC would be examples of the first type, perl, Python, MATLAB, and Ruby are examples of the second, while UCSD Pascal is an example of the third type. Source programs are compiled ahead of time and stored as machine independent code, some systems, such as Smalltalk and contemporary versions of BASIC and Java may also combine two and three. Interpreters of various types have also constructed for many languages traditionally associated with compilation, such as Algol, Fortran, Cobol. The terms interpreted language or compiled language signify that the implementation of that language is an interpreter or a compiler. A high level language is ideally an abstraction independent of particular implementations, the first interpreted high-level language was Lisp. Lisp was first implemented in 1958 by Steve Russell on an IBM704 computer, Russell had read John McCarthys paper, and realized that the Lisp eval function could be implemented in machine code. The result was a working Lisp interpreter which could be used to run Lisp programs, or more properly, Programs written in a high level language are either directly executed by some kind of interpreter or converted into machine code by a compiler for the CPU to execute. While compilers generally produce machine code directly executable by computer hardware and this is basically the same machine specific code but augmented with a symbol table with names and tags to make executable blocks identifiable and relocatable. Compiled programs will typically use building blocks kept in a library of object code modules. A linker is used to combine library files with the file of the application to form a single executable file. The object files that are used to generate an executable file are thus often produced at different times, thus, both compilers and interpreters generally turn source code into tokens, both may generate a parse tree, and both may generate immediate instructions. However, a program still runs much faster, under most circumstances, in part because compilers are designed to optimize code. This is especially true for simpler high level languages without dynamic data structures, checks, when the program is loaded for execution. Historically, most interpreter-systems have had an editor built in. This is becoming more common also for compilers, although some prefer to use an editor of their choice. During the software development cycle, programmers make frequent changes to source code, the larger the program, the longer the wait
18.
Abstraction (software engineering)
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In software engineering and computer science, abstraction is a technique for arranging complexity of computer systems. It works by establishing a level of complexity on which a person interacts with the system, the programmer works with an idealized interface and can add additional levels of functionality that would otherwise be too complex to handle. In addition, a task of sending a message across continents would be extremely complex if the programmer had to start with a piece of fiber optic cable. By using layers of complexity that have created to abstract away the physical cables and network layout, and presenting the programmer with a virtual data channel. Abstraction can apply to control or to data, Control abstraction is the abstraction of actions while data abstraction is that of data structures, Control abstraction involves the use of subroutines and control flow abstractions Data abstraction allows handling pieces of data in meaningful ways. For example, it is the motivation behind the datatype. The notion of an object in object-oriented programming can be viewed as a way to combine abstractions of data, the same abstract definition can be used as a common interface for a family of objects with different implementations and behaviors but which share the same meaning. The inheritance mechanism in object-oriented programming can be used to define a class as the common interface. The recommendation that programmers use abstractions whenever suitable in order to avoid duplication is known as the abstraction principle, the requirement that a programming language provide suitable abstractions is also called the abstraction principle. Computing mostly operates independently of the world, The hardware implements a model of computation that is interchangeable with others. The software is structured in architectures to enable humans to create the enormous systems by concentrating on a few issues at a time and these architectures are made of specific choices of abstractions. Greenspuns Tenth Rule is an aphorism on how such an architecture is both inevitable and complex, a central form of abstraction in computing is language abstraction, new artificial languages are developed to express specific aspects of a system. Computer languages can be processed with a computer, an example of this abstraction process is the generational development of programming languages from the machine language to the assembly language and the high-level language. Each stage can be used as a stone for the next stage. The language abstraction continues for example in scripting languages and domain-specific programming languages, within a programming language, some features let the programmer create new abstractions. These include subroutines, modules, polymorphism, and software components, some other abstractions such as software design patterns and architectural styles remain invisible to a translator and operate only in the design of a system. Some abstractions try to limit the range of concepts a programmer needs to be aware of, some abstractions are designed to interoperate with other abstractions - for example, a programming language may contain a foreign function interface for making calls to the lower-level language. Different programming languages provide different types of abstraction, depending on the applications for the language
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Abstract interpretation
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In computer science, abstract interpretation is a theory of sound approximation of the semantics of computer programs, based on monotonic functions over ordered sets, especially lattices. It can be viewed as an execution of a computer program which gains information about its semantics without performing all the calculations. Abstract interpretation was formalized by the French computer scientists Patrick Cousot and this section illustrates abstract interpretation by means of real-world, non-computing examples. Consider the people in a conference room, assume a unique identifier for each person in the room, like a social security number in the United States. To prove that someone is not present, all one needs to do is see if their social security number is not on the list. Since two different people cannot have the number, it is possible to prove or disprove the presence of a participant simply by looking up his or her number. However it is possible that only the names of attendees were registered, note that this imprecise information will still be adequate for most purposes, because homonyms are rare in practice. However, in all rigor, we cannot say for sure that somebody was present in the room, if the person we are looking up is a criminal, we will issue an alarm, but there is of course the possibility of issuing a false alarm. Similar phenomena will occur in the analysis of programs, if we are only interested in some specific information, say, was there a person of age n in the room. Keeping a list of all names and dates of births is unnecessary and we may safely and without loss of precision restrict ourselves to keeping a list of the participants ages. If this is too much to handle, we might keep only the age of the youngest, m and oldest person. If the question is about an age strictly lower than m or strictly higher than M, otherwise, we may only be able to say that we do not know. In the case of computing, concrete, precise information is in general not computable within finite time, abstraction is used to allow for generalized answers to questions, this simplifies the problems, making them amenable to automatic solutions. One crucial requirement is to add enough vagueness so as to make problems manageable while still retaining enough precision for answering the important questions, given a programming or specification language, abstract interpretation consists of giving several semantics linked by relations of abstraction. A semantics is a mathematical characterization of a behavior of the program. The most precise semantics, describing very closely the actual execution of the program, are called the concrete semantics, more abstract semantics are then derived, for instance, one may consider only the set of reachable states in the executions. The goal of static analysis is to derive a computable semantic interpretation at some point, for instance, one may choose to represent the state of a program manipulating integer variables by forgetting the actual values of the variables and only keeping their signs. For some elementary operations, such as multiplication, such an abstraction does not lose any precision, to get the sign of a product, it is sufficient to know the sign of the operands
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Finite-state machine
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A finite-state machine or finite-state automaton, finite automaton, or simply a state machine, is a mathematical model of computation. It is a machine that can be in exactly one of a finite number of states at any given time. The FSM can change from one state to another in response to external inputs. A FSM is defined by a list of its states, its state. The behavior of machines can be observed in many devices in modern society that perform a predetermined sequence of actions depending on a sequence of events with which they are presented. The finite state machine has less power than some other models of computation such as the Turing machine. The computational power distinction means there are tasks that a Turing machine can do. This is because a FSMs memory is limited by the number of states it has, FSMs are studied in the more general field of automata theory. An example of a mechanism that can be modeled by a machine is a turnstile. A turnstile, used to access to subways and amusement park rides, is a gate with three rotating arms at waist height, one across the entryway. Initially the arms are locked, blocking the entry, preventing patrons from passing through, depositing a coin or token in a slot on the turnstile unlocks the arms, allowing a single customer to push through. After the customer passes through, the arms are locked again until another coin is inserted, considered as a state machine, the turnstile has two possible states, Locked and Unlocked. There are two inputs that affect its state, putting a coin in the slot and pushing the arm. In the locked state, pushing on the arm has no effect, no matter how many times the input push is given, putting a coin in – that is, giving the machine a coin input – shifts the state from Locked to Unlocked. In the unlocked state, putting additional coins in has no effect, however, a customer pushing through the arms, giving a push input, shifts the state back to Locked. Each state is represented by a node, edges show the transitions from one state to another. Each arrow is labeled with the input that triggers that transition, an input that doesnt cause a change of state is represented by a circular arrow returning to the original state. The arrow into the Locked node from the dot indicates it is the initial state
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Flynn's taxonomy
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Flynns taxonomy is a classification of computer architectures, proposed by Michael J. Flynn in 1966. The classification system has stuck, and has used as a tool in design of modern processors. Since the rise of multiprocessing central processing units, a multiprogramming context has evolved as an extension of the classification system, the four classifications defined by Flynn are based upon the number of concurrent instruction streams and data streams available in the architecture. A sequential computer which exploits no parallelism in either the instruction or data streams, single control unit fetches single instruction stream from memory. The CU then generates appropriate control signals to direct single processing element to operate on single data stream i. e. one operation at a time, examples of SISD architecture are the traditional uniprocessor machines like older personal computers and mainframe computers. A computer which exploits multiple data streams against a stream to perform operations which may be naturally parallelized. For example, a processor or graphics processing unit Multiple instructions operate on one data stream. Uncommon architecture which is used for fault tolerance. Heterogeneous systems operate on the data stream and must agree on the result. Examples include the Space Shuttle flight control computer, Multiple autonomous processors simultaneously executing different instructions on different data. MIMD architectures include multi-core superscalar processors, and distributed systems, using either one shared memory space or a memory space. Single instruction, multiple threads is a model used in parallel computing where single instruction. This is not originally part of Flynns taxonomy but a proposed addition and these four architectures are shown below visually. Each processing unit is shown for a uni-core or multi-core computer, As of 2006, all the top 10, some further divide the MIMD category into the two categories below, and even further subdivisions are sometimes considered. Multiple autonomous processors simultaneously executing the program on different data. SPMD is the most common style of parallel programming, the SPMD model and the term was proposed by Frederica Darema. Gregory F. Pfister was a manager of the RP3 project, Multiple autonomous processors simultaneously operating at least 2 independent programs. Typically such systems pick one node to be the host or manager and those other nodes then return their results directly to the manager
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Bulk synchronous parallel
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The Bulk Synchronous Parallel abstract computer is a bridging model for designing parallel algorithms. It serves a similar to the Parallel Random Access Machine model. BSP differs from PRAM by not taking communication and synchronization for granted, an important part of analyzing a BSP algorithm rests on quantifying the synchronization and communication needed. The BSP model was developed by Leslie Valiant of Harvard University during the 1980s, the definitive article was published in 1990. Between 1990 and 1992, Leslie Valiant and Bill McColl of Oxford University worked on ideas for a distributed memory BSP programming model, in Princeton and at Harvard. Between 1992 and 1997, McColl led a research team at Oxford that developed various BSP programming libraries, languages and tools. Valiant developed an extension to the BSP model in the 2000s, each process can only make use of values stored in the local fast memory of the processor. The computations occur asynchronously of all the others but may overlap with communication, communication, The processes exchange data between themselves to facilitate remote data storage capabilities. Barrier synchronisation, When a process reaches this point, it waits until all other processes have reached the same barrier, the computation and communication actions do not have to be ordered in time. Communication typically takes the form of the put and get Direct Remote Memory Access calls, rather than paired two-sided send. The barrier synchronization concludes the superstep, it ensures that all one-sided communications are properly concluded, systems based on two-sided communication include this synchronisation cost implicitly for every message sent. The method for barrier synchronisation relies on the facility of the BSP computer. In Valiants original paper, this facility periodically checks if the end of the current superstep is reached globally, the period of this check is denoted by L. The figure below shows this in a diagrammatic form, the processes are not regarded as having a particular linear order, and may be mapped to processors in any way. The BSP model is also well-suited to enable automatic memory management for distributed-memory computing through overdecomposition of the problem, the computation is divided into more logical processes than there are physical processors, and processes are randomly assigned to processors. This strategy can be shown statistically to lead to almost perfect load balancing, in many parallel programming systems, communications are considered at the level of individual actions, sending and receiving a message, memory to memory transfer, etc. This is difficult to work with, since there are many simultaneous communication actions in a parallel program, in particular, it is difficult to say much about the time any single communication action will take to complete. The BSP model considers communication actions en masse and this has the effect that an upper bound on the time taken to communicate a set of data can be given
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Free On-line Dictionary of Computing
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The Free On-line Dictionary of Computing is an online, searchable, encyclopedic dictionary of computing subjects. It was founded in 1985 by Denis Howe and was hosted by Imperial College London, in May 2015, the site was updated to state that it was no longer supported by Imperial College Department of Computing. Howe has served as the editor-in-chief since the inception, with visitors to the website able to make suggestions for additions or corrections to articles. The dictionary incorporates the text of other resources, such as the Jargon File. Due to its availability under the GNU Free Documentation License, a license, it has in turn been incorporated in whole or part into other free content projects
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GNU Free Documentation License
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The GNU Free Documentation License is a copyleft license for free documentation, designed by the Free Software Foundation for the GNU Project. It is similar to the GNU General Public License, giving readers the rights to copy, redistribute, copies may also be sold commercially, but, if produced in larger quantities, the original document or source code must be made available to the works recipient. The GFDL was designed for manuals, textbooks, other reference and instructional materials, however, it can be used for any text-based work, regardless of subject matter. For example, the online encyclopedia Wikipedia uses the GFDL for all of its text. The GFDL was released in form for feedback in September 1999. After revisions, version 1.1 was issued in March 2000, version 1.2 in November 2002, the current state of the license is version 1.3. The first discussion draft of the GNU Free Documentation License version 2 was released on September 26,2006, material licensed under the current version of the license can be used for any purpose, as long as the use meets certain conditions. All previous authors of the work must be attributed, all changes to the work must be logged. All derivative works must be licensed under the same license, the full text of the license, unmodified invariant sections as defined by the author if any, and any other added warranty disclaimers and copyright notices from previous versions must be maintained. Technical measures such as DRM may not be used to control or obstruct distribution or editing of the document, the license explicitly separates any kind of Document from Secondary Sections, which may not be integrated with the Document, but exist as front-matter materials or appendices. Secondary sections can contain information regarding the authors or publishers relationship to the subject matter, if the material is modified, its title has to be changed. The license also has provisions for the handling of front-cover and back-cover texts of books, as well as for History, Acknowledgements, Dedications and Endorsements sections. These features were added in part to make the more financially attractive to commercial publishers of software documentation. Endorsements sections are intended to be used in official standard documents, the GFDL requires the ability to copy and distribute the Document in any medium, either commercially or noncommercially and therefore is incompatible with material that excludes commercial re-use. Material that restricts commercial re-use is incompatible with the license and cannot be incorporated into the work, one example of such liberal and commercial fair use is parody. Although the two work on similar copyleft principles, the GFDL is not compatible with the Creative Commons Attribution-ShareAlike license. These exemptions allow a GFDL-based collaborative project with multiple authors to transition to the CC BY-SA3, if it was not originally published on an MMC, it can only be relicensed if it were added to an MMC before November 1,2008. To prevent the clause from being used as a general compatibility measure, at the release of version 1.3, the FSF stated that all content added before November 1,2008 to Wikipedia as an example satisfied the conditions
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International Standard Book Number
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The International Standard Book Number is a unique numeric commercial book identifier. An ISBN is assigned to each edition and variation of a book, for example, an e-book, a paperback and a hardcover edition of the same book would each have a different ISBN. The ISBN is 13 digits long if assigned on or after 1 January 2007, the method of assigning an ISBN is nation-based and varies from country to country, often depending on how large the publishing industry is within a country. The initial ISBN configuration of recognition was generated in 1967 based upon the 9-digit Standard Book Numbering created in 1966, the 10-digit ISBN format was developed by the International Organization for Standardization and was published in 1970 as international standard ISO2108. Occasionally, a book may appear without a printed ISBN if it is printed privately or the author does not follow the usual ISBN procedure, however, this can be rectified later. Another identifier, the International Standard Serial Number, identifies periodical publications such as magazines, the ISBN configuration of recognition was generated in 1967 in the United Kingdom by David Whitaker and in 1968 in the US by Emery Koltay. The 10-digit ISBN format was developed by the International Organization for Standardization and was published in 1970 as international standard ISO2108, the United Kingdom continued to use the 9-digit SBN code until 1974. The ISO on-line facility only refers back to 1978, an SBN may be converted to an ISBN by prefixing the digit 0. For example, the edition of Mr. J. G. Reeder Returns, published by Hodder in 1965, has SBN340013818 -340 indicating the publisher,01381 their serial number. This can be converted to ISBN 0-340-01381-8, the check digit does not need to be re-calculated, since 1 January 2007, ISBNs have contained 13 digits, a format that is compatible with Bookland European Article Number EAN-13s. An ISBN is assigned to each edition and variation of a book, for example, an ebook, a paperback, and a hardcover edition of the same book would each have a different ISBN. The ISBN is 13 digits long if assigned on or after 1 January 2007, a 13-digit ISBN can be separated into its parts, and when this is done it is customary to separate the parts with hyphens or spaces. Separating the parts of a 10-digit ISBN is also done with either hyphens or spaces, figuring out how to correctly separate a given ISBN number is complicated, because most of the parts do not use a fixed number of digits. ISBN issuance is country-specific, in that ISBNs are issued by the ISBN registration agency that is responsible for country or territory regardless of the publication language. Some ISBN registration agencies are based in national libraries or within ministries of culture, in other cases, the ISBN registration service is provided by organisations such as bibliographic data providers that are not government funded. In Canada, ISBNs are issued at no cost with the purpose of encouraging Canadian culture. In the United Kingdom, United States, and some countries, where the service is provided by non-government-funded organisations. Australia, ISBNs are issued by the library services agency Thorpe-Bowker
