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Computer
Computer
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Black-and-white image of a man replacing one vacuum tube out of hundreds in early computer
Computer room with multiple computer cabinets and operating panel
Smartphone with rainbow-like display held in a hand
Black desktop computer with monitor on top and keyboard in front
Purple video game console with attached controller
Rows of large, dark computer cabinets in warehouse-like room
Computers and computing devices from different eras—left to right, top to bottom:

A computer is a machine that can be programmed to automatically carry out sequences of arithmetic or logical operations (computation). Modern digital electronic computers can perform generic sets of operations known as programs, which enable computers to perform a wide range of tasks. The term computer system may refer to a nominally complete computer that includes the hardware, operating system, software, and peripheral equipment needed and used for full operation; or to a group of computers that are linked and function together, such as a computer network or computer cluster.

A broad range of industrial and consumer products use computers as control systems, including simple special-purpose devices like microwave ovens and remote controls, and factory devices like industrial robots. Computers are at the core of general-purpose devices such as personal computers and mobile devices such as smartphones. Computers power the Internet, which links billions of computers and users.

Early computers were meant to be used only for calculations. Simple manual instruments like the abacus have aided people in doing calculations since ancient times. 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, both electromechanical and using thermionic valves. The first semiconductor transistors in the late 1940s were followed by the silicon-based MOSFET (MOS transistor) and monolithic integrated circuit chip technologies in the late 1950s, leading to the microprocessor and the microcomputer revolution in the 1970s. The speed, power, and versatility of computers have been increasing dramatically ever since then, with transistor counts increasing at a rapid pace (Moore's law noted that counts doubled every two years), leading to the Digital Revolution during the late 20th and early 21st centuries.

Conventionally, a modern computer consists of at least one processing element, typically a central processing unit (CPU) in the form of a microprocessor, together with some type of computer memory, typically semiconductor memory chips. The processing element carries out arithmetic and logical operations, and a sequencing and control unit can change the order of operations in response to stored information. Peripheral devices include input devices (keyboards, mice, joysticks, etc.), output devices (monitors, printers, etc.), and input/output devices that perform both functions (e.g. touchscreens). Peripheral devices allow information to be retrieved from an external source, and they enable the results of operations to be saved and retrieved.

Etymology

[edit]
A human computer.
A human computer, with microscope and calculator, 1952

It was not until the mid-20th century that the word acquired its modern definition; according to the Oxford English Dictionary, the first known use of the word computer was in a different sense, in a 1613 book called The Yong Mans Gleanings by the English writer Richard Brathwait: "I haue [sic] read the truest computer of Times, and the best Arithmetician that euer [sic] breathed, and he reduceth thy dayes into a short number." This usage of the term referred to a human computer, a person who carried out calculations or computations. The word continued to have the same meaning until the middle of the 20th century. During the latter part of this period, women were often hired as computers because they could be paid less than their male counterparts.[1] By 1943, most human computers were women.[2]

The Online Etymology Dictionary gives the first attested use of computer in the 1640s, meaning 'one who calculates'; this is an "agent noun from compute (v.)". The Online Etymology Dictionary states that the use of the term to mean "'calculating machine' (of any type) is from 1897." The Online Etymology Dictionary indicates that the "modern use" of the term, to mean 'programmable digital electronic computer' dates from "1945 under this name; [in a] theoretical [sense] from 1937, as Turing machine".[3] The name has remained, although modern computers are capable of many higher-level functions.

History

[edit]

Pre-20th century

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The Ishango bone, a bone tool dating back to prehistoric Africa

Devices have been used to aid computation for thousands of years, mostly using one-to-one correspondence with fingers. The earliest counting device was most likely a form of tally stick. Later record keeping aids throughout the Fertile Crescent included calculi (clay spheres, cones, etc.) which represented counts of items, likely livestock or grains, sealed in hollow unbaked clay containers.[a][4] The use of counting rods is one example.

The Chinese suanpan (算盘). The number represented on this abacus is 6,302,715,408.

The abacus was initially used for arithmetic tasks. The Roman abacus was developed from devices used in Babylonia as early as 2400 BCE. Since then, many other forms of reckoning boards or tables have been invented. In a medieval European counting house, a checkered cloth would be placed on a table, and markers moved around on it according to certain rules, as an aid to calculating sums of money.[5]

The Antikythera mechanism, dating back to ancient Greece circa 200–80 BCE, is an early analog computing device.

The Antikythera mechanism is believed to be the earliest known mechanical analog computer, according to Derek J. de Solla Price.[6] It was designed to calculate astronomical positions. It was discovered in 1901 in the Antikythera wreck off the Greek island of Antikythera, between Kythera and Crete, and has been dated to approximately c. 100 BCE. Devices of comparable complexity to the Antikythera mechanism would not reappear until the fourteenth century.[7]

Many mechanical aids to calculation and measurement were constructed for astronomical and navigation use. The planisphere was a star chart invented by Abū Rayhān al-Bīrūnī in the early 11th century.[8] The astrolabe was invented in the Hellenistic world in either the 1st or 2nd centuries BCE and is often attributed to Hipparchus. A combination of the planisphere and dioptra, the astrolabe was effectively an analog computer capable of working out several different kinds of problems in spherical astronomy. An astrolabe incorporating a mechanical calendar computer[9][10] and gear-wheels was invented by Abi Bakr of Isfahan, Persia in 1235.[11] Abū Rayhān al-Bīrūnī invented the first mechanical geared lunisolar calendar astrolabe,[12] an early fixed-wired knowledge processing machine[13] with a gear train and gear-wheels,[14] c. 1000 AD.

The sector, a calculating instrument used for solving problems in proportion, trigonometry, multiplication and division, and for various functions, such as squares and cube roots, was developed in the late 16th century and found application in gunnery, surveying and navigation.

The planimeter was a manual instrument to calculate the area of a closed figure by tracing over it with a mechanical linkage.

A slide rule

The slide rule was invented around 1620–1630, by the English clergyman William Oughtred, shortly after the publication of the concept of the logarithm. It is a hand-operated analog computer for doing multiplication and division. As slide rule development progressed, added scales provided reciprocals, squares and square roots, cubes and cube roots, as well as transcendental functions such as logarithms and exponentials, circular and hyperbolic trigonometry and other functions. Slide rules with special scales are still used for quick performance of routine calculations, such as the E6B circular slide rule used for time and distance calculations on light aircraft.

In the 1770s, Pierre Jaquet-Droz, a Swiss watchmaker, built a mechanical doll (automaton) that could write holding a quill pen. By switching the number and order of its internal wheels different letters, and hence different messages, could be produced. In effect, it could be mechanically "programmed" to read instructions. Along with two other complex machines, the doll is at the Musée d'Art et d'Histoire of Neuchâtel, Switzerland, and still operates.[15]

In 1831–1835, mathematician and engineer Giovanni Plana devised a Perpetual Calendar machine, which through a system of pulleys and cylinders could predict the perpetual calendar for every year from 0 CE (that is, 1 BCE) to 4000 CE, keeping track of leap years and varying day length. The tide-predicting machine invented by the Scottish scientist Sir William Thomson in 1872 was of great utility to navigation in shallow waters. It used a system of pulleys and wires to automatically calculate predicted tide levels for a set period at a particular location.

The differential analyser, a mechanical analog computer designed to solve differential equations by integration, used wheel-and-disc mechanisms to perform the integration. In 1876, Sir William Thomson had already discussed the possible construction of such calculators, but he had been stymied by the limited output torque of the ball-and-disk integrators.[16] In a differential analyzer, the output of one integrator drove the input of the next integrator, or a graphing output. The torque amplifier was the advance that allowed these machines to work. Starting in the 1920s, Vannevar Bush and others developed mechanical differential analyzers.

In the 1890s, the Spanish engineer Leonardo Torres Quevedo began to develop a series of advanced analog machines that could solve real and complex roots of polynomials,[17][18][19][20] which were published in 1901 by the Paris Academy of Sciences.[21]

First computer

[edit]
Charles Babbage
A diagram of a portion of Babbage's Difference engine
The Difference Engine Number 2 at the Intellectual Ventures laboratory in Seattle

Charles Babbage, an English mechanical engineer and polymath, originated the concept of a programmable computer. Considered the "father of the computer",[22] he conceptualized and invented the first mechanical computer in the early 19th century.

After working on his difference engine he announced his invention in 1822, in a paper to the Royal Astronomical Society, titled "Note on the application of machinery to the computation of astronomical and mathematical tables".[23] He also designed to aid in navigational calculations, in 1833 he realized that a much more general design, an analytical engine, was possible. The input of programs and data was to be provided to the machine via punched cards, a method being used at the time to direct mechanical looms such as the Jacquard loom. For output, the machine would have a printer, a curve plotter and a bell. The machine would also be able to punch numbers onto cards to be read in later. The engine would incorporate an arithmetic logic unit, control flow in the form of conditional branching and loops, and integrated memory, making it the first design for a general-purpose computer that could be described in modern terms as Turing-complete.[24][25]

The machine was about a century ahead of its time. All the parts for his machine had to be made by hand – this was a major problem for a device with thousands of parts. Eventually, the project was dissolved with the decision of the British Government to cease funding. Babbage's failure to complete the analytical engine can be chiefly attributed to political and financial difficulties as well as his desire to develop an increasingly sophisticated computer and to move ahead faster than anyone else could follow. Nevertheless, his son, Henry Babbage, completed a simplified version of the analytical engine's computing unit (the mill) in 1888. He gave a successful demonstration of its use in computing tables in 1906.

Electromechanical calculating machine

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Electro-mechanical calculator (1920) by Leonardo Torres Quevedo.

In his work Essays on Automatics published in 1914, Leonardo Torres Quevedo wrote a brief history of Babbage's efforts at constructing a mechanical Difference Engine and Analytical Engine. The paper contains a design of a machine capable to calculate formulas like , for a sequence of sets of values. The whole machine was to be controlled by a read-only program, which was complete with provisions for conditional branching. He also introduced the idea of floating-point arithmetic.[26][27][28] In 1920, to celebrate the 100th anniversary of the invention of the arithmometer, Torres presented in Paris the Electromechanical Arithmometer, which allowed a user to input arithmetic problems through a keyboard, and computed and printed the results,[29][30][31][32] demonstrating the feasibility of an electromechanical analytical engine.[33]

Analog computers

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Sir William Thomson's third tide-predicting machine design, 1879–81

During the first half of the 20th century, many scientific computing needs were met by increasingly sophisticated analog computers, which used a direct mechanical or electrical model of the problem as a basis for computation. However, these were not programmable and generally lacked the versatility and accuracy of modern digital computers.[34] The first modern analog computer was a tide-predicting machine, invented by Sir William Thomson (later to become Lord Kelvin) in 1872. The differential analyser, a mechanical analog computer designed to solve differential equations by integration using wheel-and-disc mechanisms, was conceptualized in 1876 by James Thomson, the elder brother of the more famous Sir William Thomson.[16]

The art of mechanical analog computing reached its zenith with the differential analyzer, completed in 1931 by Vannevar Bush at MIT.[35] By the 1950s, the success of digital electronic computers had spelled the end for most analog computing machines, but analog computers remained in use during the 1950s in some specialized applications such as education (slide rule) and aircraft (control systems).[citation needed]

Digital computers

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Electromechanical

[edit]

Claude Shannon's 1937 master's thesis laid the foundations of digital computing, with his insight of applying Boolean algebra to the analysis and synthesis of switching circuits being the basic concept which underlies all electronic digital computers.[36][37]

By 1938, the United States Navy had developed the Torpedo Data Computer, an electromechanical analog computer for submarines that used trigonometry to solve the problem of firing a torpedo at a moving target. During World War II, similar devices were developed in other countries.[38]

Replica of Konrad Zuse's Z3, the first fully automatic, digital (electromechanical) computer

Early digital computers were electromechanical; electric switches drove mechanical relays to perform the calculation. These devices had a low operating speed and were eventually superseded by much faster all-electric computers, originally using vacuum tubes. The Z2, created by German engineer Konrad Zuse in 1939 in Berlin, was one of the earliest examples of an electromechanical relay computer.[39]

Konrad Zuse, inventor of the modern computer[40][41]

In 1941, Zuse followed his earlier machine up with the Z3, the world's first working electromechanical programmable, fully automatic digital computer.[42][43] The Z3 was built with 2000 relays, implementing a 22 bit word length that operated at a clock frequency of about 5–10 Hz.[44] Program code was supplied on punched film while data could be stored in 64 words of memory or supplied from the keyboard. It was quite similar to modern machines in some respects, pioneering numerous advances such as floating-point numbers. Rather than the harder-to-implement decimal system (used in Charles Babbage's earlier design), using a binary system meant that Zuse's machines were easier to build and potentially more reliable, given the technologies available at that time.[45] The Z3 was not itself a universal computer but could be extended to be Turing complete.[46][47]

Zuse's next computer, the Z4, became the world's first commercial computer; after initial delay due to the Second World War, it was completed in 1950 and delivered to the ETH Zurich.[48] The computer was manufactured by Zuse's own company, Zuse KG, which was founded in 1941 as the first company with the sole purpose of developing computers in Berlin.[48] The Z4 served as the inspiration for the construction of the ERMETH, the first Swiss computer and one of the first in Europe.[49]

Vacuum tubes and digital electronic circuits

[edit]

Purely electronic circuit elements soon replaced their mechanical and electromechanical equivalents, at the same time that digital calculation replaced analog. The engineer Tommy Flowers, working at the Post Office Research Station in London in the 1930s, began to explore the possible use of electronics for the telephone exchange. Experimental equipment that he built in 1934 went into operation five years later, converting a portion of the telephone exchange network into an electronic data processing system, using thousands of vacuum tubes.[34] In the US, John Vincent Atanasoff and Clifford E. Berry of Iowa State University developed and tested the Atanasoff–Berry Computer (ABC) in 1942,[50] the first "automatic electronic digital computer".[51] This design was also all-electronic and used about 300 vacuum tubes, with capacitors fixed in a mechanically rotating drum for memory.[52]

Two women are seen by the Colossus computer.
Colossus, the first electronic digital programmable computing device, was used to break German ciphers during World War II. It is seen here in use at Bletchley Park in 1943.

During World War II, the British code-breakers at Bletchley Park achieved a number of successes at breaking encrypted German military communications. The German encryption machine, Enigma, was first attacked with the help of the electro-mechanical bombes which were often run by women.[53][54] To crack the more sophisticated German Lorenz SZ 40/42 machine, used for high-level Army communications, Max Newman and his colleagues commissioned Flowers to build the Colossus.[52] He spent eleven months from early February 1943 designing and building the first Colossus.[55] After a functional test in December 1943, Colossus was shipped to Bletchley Park, where it was delivered on 18 January 1944[56] and attacked its first message on 5 February.[52]

Colossus was the world's first electronic digital programmable computer.[34] It used a large number of valves (vacuum tubes). It had paper-tape input and was capable of being configured to perform a variety of boolean logical operations on its data, but it was not Turing-complete. Nine Mk II Colossi were built (The Mk I was converted to a Mk II making ten machines in total). Colossus Mark I contained 1,500 thermionic valves (tubes), but Mark II with 2,400 valves, was both five times faster and simpler to operate than Mark I, greatly speeding the decoding process.[57][58]

ENIAC was the first electronic, Turing-complete device, and performed ballistics trajectory calculations for the United States Army.

The ENIAC[59] (Electronic Numerical Integrator and Computer) was the first electronic programmable computer built in the U.S. Although the ENIAC was similar to the Colossus, it was much faster, more flexible, and it was Turing-complete. Like the Colossus, a "program" on the ENIAC was defined by the states of its patch cables and switches, a far cry from the stored program electronic machines that came later. Once a program was written, it had to be mechanically set into the machine with manual resetting of plugs and switches. The programmers of the ENIAC were six women, often known collectively as the "ENIAC girls".[60][61]

It combined the high speed of electronics with the ability to be programmed for many complex problems. It could add or subtract 5000 times a second, a thousand times faster than any other machine. It also had modules to multiply, divide, and square root. High speed memory was limited to 20 words (about 80 bytes). Built under the direction of John Mauchly and J. Presper Eckert at the University of Pennsylvania, ENIAC's development and construction lasted from 1943 to full operation at the end of 1945. The machine was huge, weighing 30 tons, using 200 kilowatts of electric power and contained over 18,000 vacuum tubes, 1,500 relays, and hundreds of thousands of resistors, capacitors, and inductors.[62]

Modern computers

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Concept of modern computer

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The principle of the modern computer was proposed by Alan Turing in his seminal 1936 paper,[63] On Computable Numbers. Turing proposed a simple device that he called "Universal Computing machine" and that is now known as a universal Turing machine. He proved that such a machine is capable of computing anything that is computable by executing instructions (program) stored on tape, allowing the machine to be programmable. The fundamental concept of Turing's design is the stored program, where all the instructions for computing are stored in memory. Von Neumann acknowledged that the central concept of the modern computer was due to this paper.[64] Turing machines are to this day a central object of study in theory of computation. Except for the limitations imposed by their finite memory stores, modern computers are said to be Turing-complete, which is to say, they have algorithm execution capability equivalent to a universal Turing machine.

Stored programs

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Three tall racks containing electronic circuit boards
A section of the reconstructed Manchester Baby, the first electronic stored-program computer

Early computing machines had fixed programs. Changing its function required the re-wiring and re-structuring of the machine.[52] With the proposal of the stored-program computer this changed. A stored-program computer includes by design an instruction set and can store in memory a set of instructions (a program) that details the computation. The theoretical basis for the stored-program computer was laid out by Alan Turing in his 1936 paper. In 1945, Turing joined the National Physical Laboratory and began work on developing an electronic stored-program digital computer. His 1945 report "Proposed Electronic Calculator" was the first specification for such a device. John von Neumann at the University of Pennsylvania also circulated his First Draft of a Report on the EDVAC in 1945.[34]

The Manchester Baby was the world's first stored-program computer. It was built at the University of Manchester in England by Frederic C. Williams, Tom Kilburn and Geoff Tootill, and ran its first program on 21 June 1948.[65] It was designed as a testbed for the Williams tube, the first random-access digital storage device.[66] Although the computer was described as "small and primitive" by a 1998 retrospective, it was the first working machine to contain all of the elements essential to a modern electronic computer.[67] As soon as the Baby had demonstrated the feasibility of its design, a project began at the university to develop it into a practically useful computer, the Manchester Mark 1.

The Mark 1 in turn quickly became the prototype for the Ferranti Mark 1, the world's first commercially available general-purpose computer.[68] Built by Ferranti, it was delivered to the University of Manchester in February 1951. At least seven of these later machines were delivered between 1953 and 1957, one of them to Shell labs in Amsterdam.[69] In October 1947 the directors of British catering company J. Lyons & Company decided to take an active role in promoting the commercial development of computers. Lyons's LEO I computer, modelled closely on the Cambridge EDSAC of 1949, became operational in April 1951[70] and ran the world's first routine office computer job.

Transistors

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Bipolar junction transistor (BJT)

The concept of a field-effect transistor was proposed by Julius Edgar Lilienfeld in 1925. John Bardeen and Walter Brattain, while working under William Shockley at Bell Labs, built the first working transistor, the point-contact transistor, in 1947, which was followed by Shockley's bipolar junction transistor in 1948.[71][72] From 1955 onwards, transistors replaced vacuum tubes in computer designs, giving rise to the "second generation" of computers. Compared to vacuum tubes, transistors have many advantages: they are smaller, and require less power than vacuum tubes, so give off less heat. Junction transistors were much more reliable than vacuum tubes and had longer, indefinite, service life. Transistorized computers could contain tens of thousands of binary logic circuits in a relatively compact space. However, early junction transistors were relatively bulky devices that were difficult to manufacture on a mass-production basis, which limited them to a number of specialized applications.[73]

At the University of Manchester, a team under the leadership of Tom Kilburn designed and built a machine using the newly developed transistors instead of valves.[74] Their first transistorized computer and the first in the world, was operational by 1953, and a second version was completed there in April 1955. However, the machine did make use of valves to generate its 125 kHz clock waveforms and in the circuitry to read and write on its magnetic drum memory, so it was not the first completely transistorized computer. That distinction goes to the Harwell CADET of 1955,[75] built by the electronics division of the Atomic Energy Research Establishment at Harwell.[75][76]

MOSFET (MOS transistor), showing gate (G), body (B), source (S) and drain (D) terminals. The gate is separated from the body by an insulating layer (pink).

The metal–oxide–silicon field-effect transistor (MOSFET), also known as the MOS transistor, was invented at Bell Labs between 1955 and 1960[77][78][79][80][81][82] and was the first truly compact transistor that could be miniaturized and mass-produced for a wide range of uses.[73] With its high scalability,[83] and much lower power consumption and higher density than bipolar junction transistors,[84] the MOSFET made it possible to build high-density integrated circuits.[85][86] In addition to data processing, it also enabled the practical use of MOS transistors as memory cell storage elements, leading to the development of MOS semiconductor memory, which replaced earlier magnetic-core memory in computers. The MOSFET led to the microcomputer revolution,[87] and became the driving force behind the computer revolution.[88][89] The MOSFET is the most widely used transistor in computers,[90][91] and is the fundamental building block of digital electronics.[92]

Integrated circuits

[edit]
Integrated circuits are typically packaged in plastic, metal, or ceramic cases to protect the IC from damage and for ease of assembly.

The next great advance in computing power came with the advent of the integrated circuit (IC). The idea of the integrated circuit was first conceived by a radar scientist working for the Royal Radar Establishment of the Ministry of Defence, Geoffrey W.A. Dummer. Dummer presented the first public description of an integrated circuit at the Symposium on Progress in Quality Electronic Components in Washington, D.C., on 7 May 1952.[93]

The first working ICs were invented by Jack Kilby at Texas Instruments and Robert Noyce at Fairchild Semiconductor.[94] Kilby recorded his initial ideas concerning the integrated circuit in July 1958, successfully demonstrating the first working integrated example on 12 September 1958.[95] In his patent application of 6 February 1959, Kilby described his new device as "a body of semiconductor material ... wherein all the components of the electronic circuit are completely integrated".[96][97] However, Kilby's invention was a hybrid integrated circuit (hybrid IC), rather than a monolithic integrated circuit (IC) chip.[98] Kilby's IC had external wire connections, which made it difficult to mass-produce.[99]

Noyce also came up with his own idea of an integrated circuit half a year later than Kilby.[100] Noyce's invention was the first true monolithic IC chip.[101][99] His chip solved many practical problems that Kilby's had not. Produced at Fairchild Semiconductor, it was made of silicon, whereas Kilby's chip was made of germanium. Noyce's monolithic IC was fabricated using the planar process, developed by his colleague Jean Hoerni in early 1959. In turn, the planar process was based on Carl Frosch and Lincoln Derick work on semiconductor surface passivation by silicon dioxide.[102][103][104][105][106][107]

Modern monolithic ICs are predominantly MOS (metal–oxide–semiconductor) integrated circuits, built from MOSFETs (MOS transistors).[108] The earliest experimental MOS IC to be fabricated was a 16-transistor chip built by Fred Heiman and Steven Hofstein at RCA in 1962.[109] General Microelectronics later introduced the first commercial MOS IC in 1964,[110] developed by Robert Norman.[109] Following the development of the self-aligned gate (silicon-gate) MOS transistor by Robert Kerwin, Donald Klein and John Sarace at Bell Labs in 1967, the first silicon-gate MOS IC with self-aligned gates was developed by Federico Faggin at Fairchild Semiconductor in 1968.[111] The MOSFET has since become the most critical device component in modern ICs.[108]

Die photograph of a MOS 6502, an early 1970s microprocessor integrating 3500 transistors on a single chip

The development of the MOS integrated circuit led to the invention of the microprocessor,[112][113] and heralded an explosion in the commercial and personal use of computers. While the subject of exactly which device was the first microprocessor is contentious, partly due to lack of agreement on the exact definition of the term "microprocessor", it is largely undisputed that the first single-chip microprocessor was the Intel 4004,[114] designed and realized by Federico Faggin with his silicon-gate MOS IC technology,[112] along with Ted Hoff, Masatoshi Shima and Stanley Mazor at Intel.[b][116] In the early 1970s, MOS IC technology enabled the integration of more than 10,000 transistors on a single chip.[86]

System on a Chip (SoCs) are complete computers on a microchip (or chip) the size of a coin.[117] They may or may not have integrated RAM and flash memory. If not integrated, the RAM is usually placed directly above (known as Package on package) or below (on the opposite side of the circuit board) the SoC, and the flash memory is usually placed right next to the SoC. This is done to improve data transfer speeds, as the data signals do not have to travel long distances. Since ENIAC in 1945, computers have advanced enormously, with modern SoCs (such as the Snapdragon 865) being the size of a coin while also being hundreds of thousands of times more powerful than ENIAC, integrating billions of transistors, and consuming only a few watts of power.

Mobile computers

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The first mobile computers were heavy and ran from mains power. The 50 lb (23 kg) IBM 5100 was an early example. Later portables such as the Osborne 1 and Compaq Portable were considerably lighter but still needed to be plugged in. The first laptops, such as the Grid Compass, removed this requirement by incorporating batteries – and with the continued miniaturization of computing resources and advancements in portable battery life, portable computers grew in popularity in the 2000s.[118] The same developments allowed manufacturers to integrate computing resources into cellular mobile phones by the early 2000s.

These smartphones and tablets run on a variety of operating systems and recently became the dominant computing device on the market.[119] These are powered by System on a Chip (SoCs), which are complete computers on a microchip the size of a coin.[117]

Types

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Computers can be classified in a number of different ways, including:

By architecture

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By size, form-factor and purpose

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Unconventional computers

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A computer does not need to be electronic, nor even have a processor, nor RAM, nor even a hard disk. While popular usage of the word "computer" is synonymous with a personal electronic computer,[c] a typical modern definition of a computer is: "A device that computes, especially a programmable [usually] electronic machine that performs high-speed mathematical or logical operations or that assembles, stores, correlates, or otherwise processes information."[124] According to this definition, any device that processes information qualifies as a computer.

Hardware

[edit]
Video demonstrating the standard components of a "slimline" computer

The term hardware covers all of those parts of a computer that are tangible physical objects. Circuits, computer chips, graphic cards, sound cards, memory (RAM), motherboard, displays, power supplies, cables, keyboards, printers and "mice" input devices are all hardware.

History of computing hardware

[edit]
First generation
(mechanical/electromechanical)
Calculators Pascal's calculator, Arithmometer, Difference engine, Quevedo's analytical machines
Programmable devices Jacquard loom, Analytical engine, IBM ASCC/Harvard Mark I, Harvard Mark II, IBM SSEC, Z1, Z2, Z3
Second generation
(vacuum tubes)
Calculators Atanasoff–Berry Computer, IBM 604, UNIVAC 60, UNIVAC 120
Programmable devices Colossus, ENIAC, Manchester Baby, EDSAC, Manchester Mark 1, Ferranti Pegasus, Ferranti Mercury, CSIRAC, EDVAC, UNIVAC I, IBM 701, IBM 702, IBM 650, Z22
Third generation
(discrete transistors and SSI, MSI, LSI integrated circuits)
Mainframes IBM 7090, IBM 7080, IBM System/360, BUNCH
Minicomputer HP 2116A, IBM System/32, IBM System/36, LINC, PDP-8, PDP-11
Desktop Computer HP 9100
Fourth generation
(VLSI integrated circuits)
Minicomputer VAX, IBM AS/400
4-bit microcomputer Intel 4004, Intel 4040
8-bit microcomputer Intel 8008, Intel 8080, Motorola 6800, Motorola 6809, MOS Technology 6502, Zilog Z80
16-bit microcomputer Intel 8088, Zilog Z8000, WDC 65816/65802
32-bit microcomputer Intel 80386, Pentium, Motorola 68000, ARM
64-bit microcomputer[d] Alpha, MIPS, PA-RISC, PowerPC, SPARC, x86-64, ARMv8-A
Embedded computer Intel 8048, Intel 8051
Personal computer Desktop computer, Home computer, Laptop computer, Personal digital assistant (PDA), Portable computer, Tablet PC, Wearable computer
Theoretical/experimental Quantum computer IBM Q System One
Chemical computer
DNA computing
Optical computer
Spintronics-based computer
Wetware/Organic computer

Other hardware topics

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Peripheral device (input/output) Input Mouse, keyboard, joystick, image scanner, webcam, graphics tablet, microphone
Output Monitor, printer, loudspeaker
Both Floppy disk drive, hard disk drive, optical disc drive, teleprinter
Computer buses Short range RS-232, SCSI, PCI, USB
Long range (computer networking) Ethernet, ATM, FDDI

A general-purpose computer has four main components: the arithmetic logic unit (ALU), the control unit, the memory, and the input and output devices (collectively termed I/O). These parts are interconnected by buses, often made of groups of wires. Inside each of these parts are thousands to trillions of small electrical circuits which can be turned off or on by means of an electronic switch. Each circuit represents a bit (binary digit) of information so that when the circuit is on it represents a "1", and when off it represents a "0" (in positive logic representation). The circuits are arranged in logic gates so that one or more of the circuits may control the state of one or more of the other circuits.

Input devices

[edit]

Input devices are the means by which the operations of a computer are controlled and it is provided with data. Examples include:

Output devices

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Output devices are the means by which a computer provides the results of its calculations in a human-accessible form. Examples include:

Control unit

[edit]
Diagram showing how a particular MIPS architecture instruction would be decoded by the control system

The control unit (often called a control system or central controller) manages the computer's various components; it reads and interprets (decodes) the program instructions, transforming them into control signals that activate other parts of the computer.[e] Control systems in advanced computers may change the order of execution of some instructions to improve performance.

A key component common to all CPUs is the program counter, a special memory cell (a register) that keeps track of which location in memory the next instruction is to be read from.[f]

The control system's function is as follows— this is a simplified description, and some of these steps may be performed concurrently or in a different order depending on the type of CPU:

  1. Read the code for the next instruction from the cell indicated by the program counter.
  2. Decode the numerical code for the instruction into a set of commands or signals for each of the other systems.
  3. Increment the program counter so it points to the next instruction.
  4. Read whatever data the instruction requires from cells in memory (or perhaps from an input device). The location of this required data is typically stored within the instruction code.
  5. Provide the necessary data to an ALU or register.
  6. If the instruction requires an ALU or specialized hardware to complete, instruct the hardware to perform the requested operation.
  7. Write the result from the ALU back to a memory location or to a register or perhaps an output device.
  8. Jump back to step (1).

Since the program counter is (conceptually) just another set of memory cells, it can be changed by calculations done in the ALU. Adding 100 to the program counter would cause the next instruction to be read from a place 100 locations further down the program. Instructions that modify the program counter are often known as "jumps" and allow for loops (instructions that are repeated by the computer) and often conditional instruction execution (both examples of control flow).

The sequence of operations that the control unit goes through to process an instruction is in itself like a short computer program, and indeed, in some more complex CPU designs, there is another yet smaller computer called a microsequencer, which runs a microcode program that causes all of these events to happen.

Central processing unit (CPU)

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The control unit, ALU, and registers are collectively known as a central processing unit (CPU). Early CPUs were composed of many separate components. Since the 1970s, CPUs have typically been constructed on a single MOS integrated circuit chip called a microprocessor.

Arithmetic logic unit (ALU)

[edit]

The ALU is capable of performing two classes of operations: arithmetic and logic.[125] The set of arithmetic operations that a particular ALU supports may be limited to addition and subtraction, or might include multiplication, division, trigonometry functions such as sine, cosine, etc., and square roots. Some can operate only on whole numbers (integers) while others use floating point to represent real numbers, albeit with limited precision. However, any computer that is capable of performing just the simplest operations can be programmed to break down the more complex operations into simple steps that it can perform. Therefore, any computer can be programmed to perform any arithmetic operation—although it will take more time to do so if its ALU does not directly support the operation. An ALU may also compare numbers and return Boolean truth values (true or false) depending on whether one is equal to, greater than or less than the other ("is 64 greater than 65?"). Logic operations involve Boolean logic: AND, OR, XOR, and NOT. These can be useful for creating complicated conditional statements and processing Boolean logic.

Superscalar computers may contain multiple ALUs, allowing them to process several instructions simultaneously.[126] Graphics processors and computers with SIMD and MIMD features often contain ALUs that can perform arithmetic on vectors and matrices.

Memory

[edit]
Magnetic-core memory (using magnetic cores) was the computer memory of choice in the 1960s, until it was replaced by semiconductor memory (using MOS memory cells).

A computer's memory can be viewed as a list of cells into which numbers can be placed or read. Each cell has a numbered "address" and can store a single number. The computer can be instructed to "put the number 123 into the cell numbered 1357" or to "add the number that is in cell 1357 to the number that is in cell 2468 and put the answer into cell 1595." The information stored in memory may represent practically anything. Letters, numbers, even computer instructions can be placed into memory with equal ease. Since the CPU does not differentiate between different types of information, it is the software's responsibility to give significance to what the memory sees as nothing but a series of numbers.

In almost all modern computers, each memory cell is set up to store binary numbers in groups of eight bits (called a byte). Each byte is able to represent 256 different numbers (28 = 256); either from 0 to 255 or −128 to +127. To store larger numbers, several consecutive bytes may be used (typically, two, four or eight). When negative numbers are required, they are usually stored in two's complement notation. Other arrangements are possible, but are usually not seen outside of specialized applications or historical contexts. A computer can store any kind of information in memory if it can be represented numerically. Modern computers have billions or even trillions of bytes of memory.

The CPU contains a special set of memory cells called registers that can be read and written to much more rapidly than the main memory area. There are typically between two and one hundred registers depending on the type of CPU. Registers are used for the most frequently needed data items to avoid having to access main memory every time data is needed. As data is constantly being worked on, reducing the need to access main memory (which is often slow compared to the ALU and control units) greatly increases the computer's speed.

Computer main memory comes in two principal varieties:

RAM can be read and written to anytime the CPU commands it, but ROM is preloaded with data and software that never changes, therefore the CPU can only read from it. ROM is typically used to store the computer's initial start-up instructions. In general, the contents of RAM are erased when the power to the computer is turned off, but ROM retains its data indefinitely. In a PC, the ROM contains a specialized program called the BIOS that orchestrates loading the computer's operating system from the hard disk drive into RAM whenever the computer is turned on or reset. In embedded computers, which frequently do not have disk drives, all of the required software may be stored in ROM. Software stored in ROM is often called firmware, because it is notionally more like hardware than software. Flash memory blurs the distinction between ROM and RAM, as it retains its data when turned off but is also rewritable. It is typically much slower than conventional ROM and RAM however, so its use is restricted to applications where high speed is unnecessary.[g]

In more sophisticated computers there may be one or more RAM cache memories, which are slower than registers but faster than main memory. Generally computers with this sort of cache are designed to move frequently needed data into the cache automatically, often without the need for any intervention on the programmer's part.

Input/output (I/O)

[edit]
Hard disk drives are common storage devices used with computers.

I/O is the means by which a computer exchanges information with the outside world.[128] Devices that provide input or output to the computer are called peripherals.[129] On a typical personal computer, peripherals include input devices like the keyboard and mouse, and output devices such as the display and printer. Hard disk drives, floppy disk drives and optical disc drives serve as both input and output devices. Computer networking is another form of I/O. I/O devices are often complex computers in their own right, with their own CPU and memory. A graphics processing unit might contain fifty or more tiny computers that perform the calculations necessary to display 3D graphics.[citation needed] Modern desktop computers contain many smaller computers that assist the main CPU in performing I/O. A 2016-era flat screen display contains its own computer circuitry.

Multitasking

[edit]

While a computer may be viewed as running one gigantic program stored in its main memory, in some systems it is necessary to give the appearance of running several programs simultaneously. This is achieved by multitasking, i.e. having the computer switch rapidly between running each program in turn.[130] One means by which this is done is with a special signal called an interrupt, which can periodically cause the computer to stop executing instructions where it was and do something else instead. By remembering where it was executing prior to the interrupt, the computer can return to that task later. If several programs are running "at the same time". Then the interrupt generator might be causing several hundred interrupts per second, causing a program switch each time. Since modern computers typically execute instructions several orders of magnitude faster than human perception, it may appear that many programs are running at the same time, even though only one is ever executing in any given instant. This method of multitasking is sometimes termed "time-sharing" since each program is allocated a "slice" of time in turn.[131]

Before the era of inexpensive computers, the principal use for multitasking was to allow many people to share the same computer. Seemingly, multitasking would cause a computer that is switching between several programs to run more slowly, in direct proportion to the number of programs it is running, but most programs spend much of their time waiting for slow input/output devices to complete their tasks. If a program is waiting for the user to click on the mouse or press a key on the keyboard, then it will not take a "time slice" until the event it is waiting for has occurred. This frees up time for other programs to execute so that many programs may be run simultaneously without unacceptable speed loss.

Multiprocessing

[edit]
Cray designed many supercomputers that used multiprocessing heavily.

Some computers are designed to distribute their work across several CPUs in a multiprocessing configuration, a technique once employed in only large and powerful machines such as supercomputers, mainframe computers and servers. Multiprocessor and multi-core (multiple CPUs on a single integrated circuit) personal and laptop computers are now widely available, and are being increasingly used in lower-end markets as a result.

Supercomputers in particular often have highly unique architectures that differ significantly from the basic stored-program architecture and from general-purpose computers.[h] They often feature thousands of CPUs, customized high-speed interconnects, and specialized computing hardware. Such designs tend to be useful for only specialized tasks due to the large scale of program organization required to use most of the available resources at once. Supercomputers usually see usage in large-scale simulation, graphics rendering, and cryptography applications, as well as with other so-called "embarrassingly parallel" tasks.

Software

[edit]

Software is the part of a computer system that consists of the encoded information that determines the computer's operation, such as data or instructions on how to process the data. In contrast to the physical hardware from which the system is built, software is immaterial. Software includes computer programs, libraries and related non-executable data, such as online documentation or digital media. It is often divided into system software and application software. Computer hardware and software require each other and neither is useful on its own. When software is stored in hardware that cannot easily be modified, such as with BIOS ROM in an IBM PC compatible computer, it is sometimes called "firmware".

Operating system / system software Unix and BSD UNIX System V, IBM AIX, HP-UX, Solaris (SunOS), IRIX, List of BSD operating systems
Linux List of Linux distributions, Comparison of Linux distributions
Windows Windows 95, Windows 98, Windows NT, Windows 2000, Windows ME, Windows XP, Windows Vista, Windows 7, Windows 8, Windows 8.1, Windows 10, Windows 11
MS-DOS compatible MS-DOS, IBM PC DOS, DR-DOS, FreeDOS
Mac Classic Mac OS, macOS (previously OS X and Mac OS X)
Embedded and real-time List of embedded operating systems
Experimental Amoeba, OberonAOS, Bluebottle, A2, Plan 9 from Bell Labs
Library Multimedia DirectX, OpenGL, OpenAL, Vulkan (API)
Programming library C standard library, Standard Template Library
Data Protocol TCP/IP, Kermit, FTP, HTTP, SMTP
File format HTML, XML, JPEG, MPEG, PNG
User interface Graphical user interface (WIMP) Windows, GNOME, KDE, QNX Photon, CDE, GEM, Aqua
Text-based user interface Command-line interface, Text user interface
Application software Office suite Word processing, Desktop publishing, Presentation program, Database management system, Scheduling & Time management, Spreadsheet, Accounting software
Internet Access Browser, Email client, Web server, Mail transfer agent, Instant messaging
Design and manufacturing Computer-aided design, Computer-aided manufacturing, Plant management, Robotic manufacturing, Supply chain management
Graphics Raster graphics editor, Vector graphics editor, 3D modeler, Animation editor, 3D computer graphics, Video editing, Image processing
Audio Digital audio editor, Audio playback, Mixing, Audio synthesis, Computer music
Software engineering Compiler, Assembler, Interpreter, Debugger, Text editor, Integrated development environment, Software performance analysis, Revision control, Software configuration management
Educational Edutainment, Educational game, Serious game, Flight simulator
Games Strategy, Arcade, Puzzle, Simulation, First-person shooter, Platform, Massively multiplayer, Interactive fiction
Misc Artificial intelligence, Antivirus software, Malware scanner, Installer/Package management systems, File manager

Programs

[edit]

The defining feature of modern computers which distinguishes them from all other machines is that they can be programmed. That is to say that some type of instructions (the program) can be given to the computer, and it will process them. Modern computers based on the von Neumann architecture often have machine code in the form of an imperative programming language. In practical terms, a computer program may be just a few instructions or extend to many millions of instructions, as do the programs for word processors and web browsers for example. A typical modern computer can execute billions of instructions per second (gigaflops) and rarely makes a mistake over many years of operation. Large computer programs consisting of several million instructions may take teams of programmers years to write, and due to the complexity of the task almost certainly contain errors.

Stored program architecture

[edit]
Replica of the Manchester Baby, the world's first electronic stored-program computer, at the Museum of Science and Industry in Manchester, England

This section applies to most common RAM machine–based computers.

In most cases, computer instructions are simple: add one number to another, move some data from one location to another, send a message to some external device, etc. These instructions are read from the computer's memory and are generally carried out (executed) in the order they were given. However, there are usually specialized instructions to tell the computer to jump ahead or backwards to some other place in the program and to carry on executing from there. These are called "jump" instructions (or branches). Furthermore, jump instructions may be made to happen conditionally so that different sequences of instructions may be used depending on the result of some previous calculation or some external event. Many computers directly support subroutines by providing a type of jump that "remembers" the location it jumped from and another instruction to return to the instruction following that jump instruction.

Program execution might be likened to reading a book. While a person will normally read each word and line in sequence, they may at times jump back to an earlier place in the text or skip sections that are not of interest. Similarly, a computer may sometimes go back and repeat the instructions in some section of the program over and over again until some internal condition is met. This is called the flow of control within the program and it is what allows the computer to perform tasks repeatedly without human intervention.

Comparatively, a person using a pocket calculator can perform a basic arithmetic operation such as adding two numbers with just a few button presses. But to add together all of the numbers from 1 to 1,000 would take thousands of button presses and a lot of time, with a near certainty of making a mistake. On the other hand, a computer may be programmed to do this with just a few simple instructions. The following example is written in the MIPS assembly language:

  begin:
  addi $8, $0, 0           # initialize sum to 0
  addi $9, $0, 1           # set first number to add = 1
  loop:
  slti $10, $9, 1000       # check if the number is less than 1000
  beq $10, $0, finish      # if odd number is greater than n then exit
  add $8, $8, $9           # update sum
  addi $9, $9, 1           # get next number
  j loop                   # repeat the summing process
  finish:
  add $2, $8, $0           # put sum in output register

Once told to run this program, the computer will perform the repetitive addition task without further human intervention. It will almost never make a mistake and a modern PC can complete the task in a fraction of a second.

Machine code

[edit]

In most computers, individual instructions are stored as machine code with each instruction being given a unique number (its operation code or opcode for short). The command to add two numbers together would have one opcode; the command to multiply them would have a different opcode, and so on. The simplest computers are able to perform any of a handful of different instructions; the more complex computers have several hundred to choose from, each with a unique numerical code. Since the computer's memory is able to store numbers, it can also store the instruction codes. This leads to the important fact that entire programs (which are just lists of these instructions) can be represented as lists of numbers and can themselves be manipulated inside the computer in the same way as numeric data. The fundamental concept of storing programs in the computer's memory alongside the data they operate on is the crux of the von Neumann, or stored program, architecture.[133][134] In some cases, a computer might store some or all of its program in memory that is kept separate from the data it operates on. This is called the Harvard architecture after the Harvard Mark I computer. Modern von Neumann computers display some traits of the Harvard architecture in their designs, such as in CPU caches.

While it is possible to write computer programs as long lists of numbers (machine language) and while this technique was used with many early computers,[i] it is extremely tedious and potentially error-prone to do so in practice, especially for complicated programs. Instead, each basic instruction can be given a short name that is indicative of its function and easy to remember – a mnemonic such as ADD, SUB, MULT or JUMP. These mnemonics are collectively known as a computer's assembly language. Converting programs written in assembly language into something the computer can actually understand (machine language) is usually done by a computer program called an assembler.

A 1970s punched card containing one line from a Fortran program. The card reads: "Z(1) = Y + W(1)" and is labeled "PROJ039" for identification purposes.

Programming language

[edit]

A programming language is a notation system for writing the source code from which a computer program is produced. Programming languages provide various ways of specifying programs for computers to run. Unlike natural languages, programming languages are designed to permit no ambiguity and to be concise. They are purely written languages and are often difficult to read aloud. They are generally either translated into machine code by a compiler or an assembler before being run, or translated directly at run time by an interpreter. Sometimes programs are executed by a hybrid method of the two techniques.

There are thousands of programming languages—some intended for general purpose programming, others useful for only highly specialized applications.

Programming languages
Lists of programming languages Timeline of programming languages, List of programming languages by category, Generational list of programming languages, List of programming languages, Non-English-based programming languages
Commonly used assembly languages ARM, MIPS, x86
Commonly used high-level programming languages Ada, BASIC, C, C++, C#, COBOL, Fortran, PL/I, REXX, Java, Lisp, Pascal, Object Pascal
Commonly used scripting languages Bourne script, JavaScript, Python, Ruby, PHP, Perl
Low-level languages
[edit]

Machine languages and the assembly languages that represent them (collectively termed low-level programming languages) are generally unique to the particular architecture of a computer's central processing unit (CPU). For instance, an ARM architecture CPU (such as may be found in a smartphone or a hand-held videogame) cannot understand the machine language of an x86 CPU that might be in a PC.[j] Historically a significant number of other CPU architectures were created and saw extensive use, notably including the MOS Technology 6502 and 6510 in addition to the Zilog Z80.

High-level languages
[edit]

Although considerably easier than in machine language, writing long programs in assembly language is often difficult and is also error prone. Therefore, most practical programs are written in more abstract high-level programming languages that are able to express the needs of the programmer more conveniently (and thereby help reduce programmer error). High level languages are usually "compiled" into machine language (or sometimes into assembly language and then into machine language) using another computer program called a compiler.[k] High level languages are less related to the workings of the target computer than assembly language, and more related to the language and structure of the problem(s) to be solved by the final program. It is therefore often possible to use different compilers to translate the same high level language program into the machine language of many different types of computer. This is part of the means by which software like video games may be made available for different computer architectures such as personal computers and various video game consoles.

Program design

[edit]

Program design of small programs is relatively simple and involves the analysis of the problem, collection of inputs, using the programming constructs within languages, devising or using established procedures and algorithms, providing data for output devices and solutions to the problem as applicable.[135] As problems become larger and more complex, features such as subprograms, modules, formal documentation, and new paradigms such as object-oriented programming are encountered.[136] Large programs involving thousands of line of code and more require formal software methodologies.[137] The task of developing large software systems presents a significant intellectual challenge.[138] Producing software with an acceptably high reliability within a predictable schedule and budget has historically been difficult;[139] the academic and professional discipline of software engineering concentrates specifically on this challenge.[140]

Bugs

[edit]
The actual first computer bug, a moth found trapped on a relay of the Harvard Mark II computer

Errors in computer programs are called "bugs". They may be benign and not affect the usefulness of the program, or have only subtle effects. However, in some cases they may cause the program or the entire system to "hang", becoming unresponsive to input such as mouse clicks or keystrokes, to completely fail, or to crash.[141] Otherwise benign bugs may sometimes be harnessed for malicious intent by an unscrupulous user writing an exploit, code designed to take advantage of a bug and disrupt a computer's proper execution. Bugs are usually not the fault of the computer. Since computers merely execute the instructions they are given, bugs are nearly always the result of programmer error or an oversight made in the program's design.[l] Admiral Grace Hopper, an American computer scientist and developer of the first compiler, is credited for having first used the term "bugs" in computing after a dead moth was found shorting a relay in the Harvard Mark II computer in September 1947.[142]

Networking and the Internet

[edit]
Visualization of a portion of the routes on the Internet

Computers have been used to coordinate information between multiple physical locations since the 1950s. The U.S. military's SAGE system was the first large-scale example of such a system, which led to a number of special-purpose commercial systems such as Sabre.[143]

In the 1970s, computer engineers at research institutions throughout the United States began to link their computers together using telecommunications technology. The effort was funded by ARPA (now DARPA), and the computer network that resulted was called the ARPANET.[144] Logic gates are a common abstraction which can apply to most of the above digital or analog paradigms. The ability to store and execute lists of instructions called programs makes computers extremely versatile, distinguishing them from calculators. The Church–Turing thesis is a mathematical statement of this versatility: any computer with a minimum capability (being Turing-complete) is, in principle, capable of performing the same tasks that any other computer can perform. Therefore, any type of computer (netbook, supercomputer, cellular automaton, etc.) is able to perform the same computational tasks, given enough time and storage capacity.

Artificial intelligence

[edit]

In the 20th century, artificial intelligence systems were predominantly symbolic: they executed code that was explicitly programmed by software developers.[145] Machine learning models, however, have a set parameters that are adjusted throughout training, so that the model learns to accomplish a task based on the provided data. The efficiency of machine learning (and in particular of neural networks) has rapidly improved with progress in hardware for parallel computing, mainly graphics processing units (GPUs).[146] Some large language models are able to control computers or robots.[147][148] AI progress may lead to the creation of artificial general intelligence (AGI), a type of AI that could accomplish virtually any intellectual task at least as well as humans.[149]

Professions and organizations

[edit]

As the use of computers has spread throughout society, there are an increasing number of careers involving computers.

Computer-related professions
Hardware-related Electrical engineering, Electronic engineering, Computer engineering, Telecommunications engineering, Optical engineering, Nanoengineering
Software-related Computer science, Computer engineering, Desktop publishing, Human–computer interaction, Information technology, Information systems, Computational science, Software engineering, Video game industry, Web design

The need for computers to work well together and to be able to exchange information has spawned the need for many standards organizations, clubs and societies of both a formal and informal nature.

Organizations
Standards groups ANSI, IEC, IEEE, IETF, ISO, W3C
Professional societies ACM, AIS, IET, IFIP, BCS
Free/open source software groups Free Software Foundation, Mozilla Foundation, Apache Software Foundation

See also

[edit]

Notes

[edit]

References

[edit]

Sources

[edit]
[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
A computer is a device that accepts and manipulates the information based on a program or sequence of instructions for how is to be processed. The term "computer" originated in the 1640s to describe a who performed calculations or reckoning, derived from the Latin computare meaning "to calculate" or "to count up together." By the mid-20th century, particularly after , the word shifted to refer to electronic machines designed for automated computation, marking the transition from manual to mechanical and then digital processing. Modern computers are typically electronic digital systems following the von Neumann architecture, a foundational design proposed in 1945 that separates processing from storage but integrates instructions and data in a unified memory. This architecture comprises key components: a central processing unit (CPU) that executes instructions via its arithmetic logic unit (ALU) for computations and control unit for orchestration; memory (such as RAM for temporary storage and secondary storage like hard drives for persistent data); and input/output (I/O) devices for interfacing with users and external systems, including keyboards, displays, and networks. Early milestones include Konrad Zuse's Z3 in 1941, the first functional programmable electromechanical computer using relays; the Colossus machines at Bletchley Park in 1943, the world's first programmable electronic digital computers using vacuum tubes for special-purpose cryptanalysis of Lorenz ciphers, which helped shorten World War II; and the ENIAC in 1945–1946, a general-purpose electronic computer with 18,000 vacuum tubes that performed calculations 1,000 times faster than mechanical predecessors. The advent of the microprocessor in 1971 by Intel revolutionized the field, enabling compact, affordable personal computers like the IBM PC in 1981, which sold over 1 million units and democratized computing. Computers encompass diverse forms, from mainframes for large-scale data processing to personal computers (PCs), laptops, smartphones, and embedded systems in appliances, all powered by software that ranges from operating systems like Windows or to applications for specific tasks. Their evolution has been driven by advances in semiconductor technology, following , which observed that counts on chips roughly double every two years, exponentially increasing computational power while reducing costs. Since the era, computers have profoundly shaped society by accelerating scientific research, transforming communication through the , automating industries, and raising ethical challenges in , cybersecurity, and automation's socioeconomic effects.

Etymology and History

Etymology

The word "computer" originates from the Latin verb computare, meaning "to calculate together" or "to reckon," derived from the prefix com- (together) and putare (to think, clean, or ). This etymological root reflects the act of reckoning or , as seen in ancient Roman texts where it involved balancing ledgers or performing arithmetic. The term entered the English language in the early 17th century to describe a human performer of calculations. Its first recorded use appears in 1613 in Richard Brathwaite's The Yong Mans Gleanings, where it denotes a person skilled in reckoning or computing figures, such as in navigation or finance. By the 18th and 19th centuries, "computer" commonly referred to individuals—often women employed in "computing rooms"—who manually executed repetitive mathematical tasks, including the preparation of logarithmic and astronomical tables for scientific and engineering purposes. During the , as mechanical calculating devices proliferated, the terminology began evolving to encompass machines that automated human computation. Early references applied "computer" to such devices, distinguishing them from manual labor; for instance, tide-predicting mechanisms and difference engines were precursors that highlighted the potential for mechanized reckoning. This shift accelerated with the advent of electromechanical systems in the early , fully redefining "computer" by the to denote electronic programmable apparatus rather than solely human operators. A key terminological distinction arose between "" and "," emphasizing programmability. Calculators, like 19th-century mechanical aids such as the , performed fixed arithmetic operations without alteration. In contrast, computers enable general-purpose computation through stored instructions, a concept advanced by Charles Babbage's 1837 design, which introduced punched cards for sequencing operations and profoundly shaped modern usage of the term.

Early Concepts and Mechanical Devices

The , recognized as one of the earliest mechanical aids for arithmetic calculations, emerged around 2400 BCE in ancient , where it facilitated addition, subtraction, multiplication, and division through sliding beads on rods or wires. This device represented numerical values in a positional system and remained in use across various cultures, evolving into forms like the Chinese suanpan by the 2nd century BCE. Similarly, the , an intricate bronze gearwork device dated to approximately 100 BCE, served as an for predicting astronomical positions, including the movements of the sun, , and , as well as eclipses, demonstrating early mechanical simulation of complex cycles. Discovered in 1901 from a off the Greek island of , it utilized at least 30 meshing bronze gears to model celestial phenomena with remarkable precision for its era. In the , advancements in mechanical calculation addressed the tedium of manual arithmetic, particularly for taxation and scientific work. invented the in 1642, a compact box with interlocking dials and gears that performed and on multi-digit numbers up to eight figures, driven by a hand crank to carry over values automatically. Approximately 50 units were produced, though its fragility limited widespread adoption. Building on this, developed the in 1673, an ambitious cylindrical gear-based machine capable of all four basic arithmetic operations—, , , and division—using a stepped mechanism to select digit values in a single revolution. Despite mechanical unreliability, such as jamming gears, it introduced key principles of and automated carrying that influenced later designs. The marked a shift toward programmable machinery, inspired by industrial . patented his in 1804, incorporating punched cards strung together to control the raising of warp threads, enabling the automated weaving of intricate patterns without manual intervention and serving as a direct precursor to stored-program concepts in . This innovation reduced labor and error in textile production, influencing data encoding methods. proposed in 1822 to automate the computation and printing of mathematical tables, using finite differences and mechanical levers to calculate polynomials without multiplication or division, though only a partial prototype was built due to funding issues. Evolving this idea, Babbage conceptualized the in 1837, a general-purpose device with a central processing unit-like mill, memory store, and conditional branching, programmable via sequences of punched cards borrowed from the Jacquard to execute arbitrary instructions. In her extensive notes accompanying a translation of an 1842 memoir on the engine, detailed its potential in 1843, including the first published —a step-by-step plan for computing Bernoulli numbers using loops and subroutines—highlighting its capacity beyond mere calculation to manipulate symbols like music or graphics.

Electromechanical and Analog Era

The electromechanical era of computing emerged in the late 19th and early 20th centuries, bridging mechanical devices with electrical components to automate and numerical calculations. A pivotal development was Herman Hollerith's electric , introduced in 1890 for the U.S. . This system used punched cards to represent demographic data, with electrically operated components that read the holes via conductive brushes, enabling rapid tabulation and sorting of over 62 million cards in under three years—far faster than manual methods. Hollerith's invention, patented in 1889, not only accelerated processing but also laid the groundwork for industries; his Company, founded in 1896, merged in 1911 to form the , which was renamed International Business Machines (IBM) in 1924. These machines represented an early fusion of electromechanical relays and mechanical counters, influencing subsequent punched-card systems for business and scientific applications. Advancing beyond tabulation, electromechanical devices tackled complex mathematical problems through analog simulation. In 1927, at MIT initiated the design of the first large-scale differential analyzer, completed between 1930 and 1931, which mechanically solved ordinary differential equations up to sixth order or three simultaneous second-order equations. The machine integrated mechanical integrators—disk-and-ball mechanisms that computed integrals by friction-driven rotation—linked via shafts and gears to model dynamic systems like ballistic trajectories and structural vibrations. Operational until the , it processed inputs via hand-cranked wheels and output continuous curves on graphical plotters, demonstrating the potential of interconnected mechanical elements for simulations. This analyzer, comprising over 100 components and weighing several tons, highlighted the era's shift toward programmable analog , though its setup time limited it to specialized tasks. Analog computers, relying on continuous physical phenomena to model mathematical relationships, further exemplified this period's innovations. One early example was the invented by William Thomson (later ) in 1872, which synthesized tidal patterns by summing up to ten harmonic components using mechanical linkages, pulleys, and rotating shafts to drive a pen across graph paper. Although designed in the , improved versions operated into the , including U.S. and Geodetic Survey models from 1883 to 1910 that predicted tides for navigation with accuracies sufficient for coastal charting. In the 1940s, electronic analog computing advanced with George A. Philbrick's development of vacuum-tube operational amplifiers, first commercialized as the Model K2-W in but prototyped earlier for wartime applications. These amplifiers, using feedback circuits to perform , integration, and on continuous voltage signals, formed the building blocks of general-purpose analog computers, enabling simulations of control systems and electrical networks with real-time responsiveness. A notable application of analog principles in non-electronic form was the Monetary National Income Analogue Computer (MONIAC), built in 1949 by economist Bill Phillips to model Keynesian economic flows. This hydraulic device used transparent tanks, pipes, and valves to represent money circulation: water levels symbolized stock variables like savings and income, while flows mimicked expenditures and investments, allowing visual demonstration of effects on a national economy. Demonstrated at the London School of Economics, the MONIAC illustrated macroeconomic dynamics through , processing inputs like to predict outputs such as GDP changes, though it required manual adjustments for different scenarios. Despite their ingenuity, electromechanical and analog systems had inherent limitations compared to emerging digital technologies, primarily due to their reliance on continuous signals versus discrete representations. Analog devices modeled problems using proportional physical quantities—such as voltages or fluid flows—that inherently introduced , drift, and scaling errors, reducing precision over time and making exact challenging. In contrast, digital systems process discrete binary states, enabling correction and arbitrary precision without physical degradation, which ultimately favored and reliability in general-purpose . These constraints confined analog machines to specific, real-time simulations, paving the way for digital paradigms in the mid-20th century.

Birth of Digital Computing

The birth of digital computing marked a pivotal shift from the limitations of analog and electromechanical systems, which struggled with precision and scalability in handling discrete , toward electronic machines capable of rapid, programmable calculations. This era, spanning the early during , saw the development of pioneering devices that laid the foundation for modern computing by employing binary representation and electronic components for arithmetic operations. In 1941, German engineer completed the Z3, recognized as the first functional program-controlled digital computer. Built using electromechanical relays for logic operations and binary encoding for data, the Z3 performed and was programmable via punched film strips, enabling it to solve complex equations automatically. Zuse's design emphasized reliability through binary logic, distinguishing it from earlier decimal-based mechanical calculators, though its relay-based construction limited its clock speed to about 5-10 Hz. The following year, in 1942, American physicists and Clifford Berry constructed the Atanasoff-Berry Computer (ABC) at Iowa State College, which is credited as the first electronic digital computer. Utilizing approximately 300 vacuum tubes for binary arithmetic and logic, the ABC solved systems of up to 29 linear equations by employing electronic switching for and subtraction, with rotating drums serving as memory. Unlike the Z3, it relied entirely on rather than relays, achieving speeds of 30 additions per second, but it was not programmable in the general sense and focused solely on specific linear algebra problems. By 1943–1944, British engineer developed Colossus at for wartime code-breaking efforts against German Lorenz ciphers. The initial Colossus machine incorporated 1,500–1,800 vacuum tubes (valves) for electronic processing, with later versions using up to 2,500, enabling programmable reconfiguration via switches and plugs to analyze encrypted traffic at speeds of 5,000 characters per second. While highly influential in —contributing to shortening the by an estimated two years—Colossus was specialized for pattern-matching tasks and lacked general-purpose capabilities. Culminating this formative period, the (Electronic Numerical Integrator and Computer), designed by and at the , became operational in 1945 as the first general-purpose electronic digital computer. Funded by the U.S. Army Ordnance Department, it used 18,000 vacuum tubes to compute artillery firing tables for ballistic trajectories, performing 5,000 additions per second across 40 panels occupying 1,800 square feet. Programming required manual rewiring of patch cords and switches, a labor-intensive process that took days, yet ENIAC's versatility extended to nuclear and simulations, demonstrating the potential of electronic digital systems for diverse applications.

Post-War Developments and Transistors

Following , the development of stored-program computers marked a pivotal shift in computing design, enabling greater flexibility and efficiency. In 1945, drafted a report on the proposed computer while at the , outlining a architecture where both data and instructions were stored in the same memory, facilitating the fetch-execute cycle—a process in which the retrieves an instruction from memory, decodes it, and executes it before incrementing the for the next step. This concept addressed the limitations of prior machines like , which relied on fixed wiring for programs and required physical reconfiguration for new tasks. The report, circulated informally in 1945 and published in 1946, became foundational for modern computer design, influencing subsequent systems by separating hardware from specific programming tasks. The first practical implementation of a occurred in 1948 with the Manchester Small-Scale Experimental Machine, known as the "Baby," developed at the by Frederic C. Williams, Tom Kilburn, and Geoffrey Tootill. On June 21, 1948, the Baby successfully executed its inaugural program—a 17-instruction routine to find the highest factor of a number—using a Williams-Kilburn tube for 32 words of memory, demonstrating the viability of electronic random-access storage for both instructions and data. This prototype, operational for research purposes, paved the way for more advanced machines like the , confirming the stored-program paradigm's potential for general-purpose computing without mechanical reconfiguration. Commercial adoption of stored-program principles accelerated with the , delivered to the U.S. Census Bureau in 1951 as the first general-purpose electronic digital computer available for purchase. Designed by and , the processed data for the 1950 U.S. Census, completing tabulations that would have taken years manually in just months, and featured drives for , and auxiliary storage, holding up to 1,000 characters per reel at speeds of 12,000 characters per second. With a main memory of 1,000 words using mercury delay lines, it performed approximately 1,905 additions per second and represented a milestone in transitioning computing from military to civilian applications. Parallel to these advances, the invention of the transistor in 1947 revolutionized computer hardware by replacing fragile vacuum tubes. At Bell Laboratories, physicists John Bardeen and Walter Brattain, under William Shockley's direction, demonstrated the first point-contact transistor on December 23, 1947, using germanium to amplify signals with a three-electrode structure that controlled current flow more efficiently than tubes. This solid-state device, awarded the Nobel Prize in Physics in 1956 to Bardeen, Brattain, and Shockley, enabled the construction of fully transistorized computers, beginning with the TRADIC (Transistorized Airborne Digital Computer) in 1954. Developed by Bell Labs for the U.S. Air Force, TRADIC used 800 point-contact transistors and 2,500 diodes for logic, core memory for 256 words, and consumed only 100 watts—far less than vacuum-tube equivalents—while fitting into a compact airborne system for navigation and bombing calculations. By the late 1950s, transistors had become standard in commercial systems, as seen in the IBM 7090, introduced in 1959 as a high-performance scientific computer. The 7090 employed over 19,500 alloy-junction transistors for logic circuits, delivering up to 229,000 instructions per second—about six times faster than its vacuum-tube predecessor, the IBM 709—and supported magnetic core memory of 32,768 words, making it suitable for applications like weather forecasting and nuclear simulations at sites such as General Electric and NASA. The transistor's adoption dramatically reduced computer size, from room-filling cabinets to more desk-compatible units; lowered power consumption from kilowatts to hundreds of watts, minimizing heat and cooling needs; cut costs through mass production and simpler manufacturing; and boosted reliability, with mean time between failures extending from hours to thousands of hours due to fewer failure-prone components. These improvements spurred the proliferation of second-generation computers, transforming computing from specialized tools to accessible technologies.

Integrated Circuits and Microprocessors

The invention of the (IC) marked a pivotal advancement in computer miniaturization during the late 1950s. In September 1958, , an engineer at , demonstrated the first working IC, a monolithic device that integrated multiple transistors, resistors, and capacitors on a single substrate, addressing the challenge of interconnecting discrete components. This breakthrough was followed in 1959 by at , who developed and patented the first practical monolithic IC using and the planar process, enabling reliable through diffused junctions and metal interconnects. These innovations built on the reliability gains of post-war transistors, reducing size and cost while increasing circuit density. In 1965, , then at Fairchild, observed in his seminal paper that the number of transistors on an IC would double approximately every year, a prediction later revised to every two years, which became known as and guided the semiconductor industry's scaling for decades. The adoption of ICs transformed mainframe computing in the 1960s, enabling more powerful and compatible systems. IBM's System/360, announced in April 1964, was the first commercial computer family to incorporate IC technology extensively, using hybrid-integrated circuits to achieve a unified across models ranging from small-scale to large-scale processors. This allowed software compatibility and , replacing IBM's disparate product lines and establishing a standard for enterprise computing that supported business applications and scientific calculations with improved performance and reduced manufacturing costs. The emerged in the early 1970s as a single-chip CPU, further accelerating . In 1971, introduced the 4004, a 4-bit designed by Marcian "Ted" Hoff, , and Stanley Mazor, containing 2,300 transistors and operating at 740 kHz, initially developed for a Japanese manufacturer (). This device integrated the core functions of a —arithmetic logic, control, and registers—onto one chip, reducing the complexity of building computers from multiple ICs and paving the way for embedded systems and programmable logic. The microprocessor's impact extended to personal computing by the mid-1970s, igniting a hobbyist revolution. The , released in 1975 by (MITS), was the first commercially successful kit, powered by the microprocessor (an 8-bit evolution of the 4004 with 6,000 transistors) and sold for $397 in kit form. Its appearance in magazine inspired entrepreneurs and to develop and license a for the Altair, enabling user-friendly programming and founding , which fueled the home computer movement and software ecosystem.

Types and Architectures

By Data Processing Method

Computers are classified by their data processing methods, which determine how is represented, manipulated, and computed. The primary categories include digital, analog, hybrid, and , each leveraging distinct physical principles to handle . This emphasizes the underlying computational rather than physical size or application, influencing their suitability for various tasks from general-purpose calculation to specialized simulations. Digital computers process in discrete binary states, typically represented as 0s and 1s, using electronic circuits that operate on binary logic to perform arithmetic, logical, and control operations. This discrete approach enables precise, programmable computation and forms the basis for nearly all modern general-purpose computing, from personal devices to supercomputers. A key subtype is the , which uses a single bus for both instructions and , facilitating sequential but potentially introducing bottlenecks during simultaneous access. In contrast, the employs separate memory spaces and pathways for instructions and , allowing parallel fetching and execution for improved performance in embedded systems and digital signal processors. Analog computers, by contrast, operate on continuous physical quantities such as voltage levels, mechanical motion, or flow to model and solve problems, particularly those involving differential equations and real-time simulations. These systems excel in approximating dynamic processes like electrical circuits or , where outputs directly correspond to input variations without . Historically prominent in engineering and scientific applications, analog computers have persisted in niche modern roles, such as (op-amp) circuits for in and control systems, offering high-speed at the cost of lower precision compared to digital methods. Hybrid computers integrate digital and analog components to leverage the precision and programmability of digital processing with the speed and continuity of analog simulation, making them ideal for complex, real-time modeling. The digital subsystem typically handles control, logic, and data conversion, while the analog portion performs continuous computations. A seminal example is the HYDAC 2400, developed by Electronic Associates in 1963, which combined a general-purpose analog computer with a digital processor for applications like aerospace simulations of re-entry vehicle flight control systems. These systems were particularly valuable in mid-20th-century engineering for tasks requiring both iterative digital optimization and analog differential equation solving. Quantum computers represent an emerging paradigm that processes information using quantum bits (qubits) governed by principles of quantum mechanics, including superposition—where qubits exist in multiple states simultaneously—and entanglement, which correlates qubit states for parallel operations across vast possibility spaces. Unlike classical systems, this enables exponential computational advantages for specific problems, such as factoring large numbers or simulating molecular interactions. As of 2025, quantum computers operate primarily in the Noisy Intermediate-Scale Quantum (NISQ) era, characterized by 50–1000 qubits with limited error correction, as exemplified by IBM's Quantum systems like the Nighthawk processor announced in November 2025, which supports hybrid quantum-classical algorithms for research in optimization and chemistry. Full fault-tolerant quantum computing remains a future goal, with ongoing advancements in error mitigation extending NISQ utility, including IBM's roadmap targeting quantum advantage by the end of 2026.

By Size and Purpose

Computers are categorized by their physical scale and primary intended applications, ranging from massive systems designed for extreme computational demands to compact, specialized units integrated into everyday devices. This classification emphasizes the trade-offs between processing power, reliability, and efficiency tailored to specific use cases, such as scientific simulations or industrial automation. Supercomputers represent the largest scale of computing systems, engineered for high-performance parallel processing to tackle complex simulations that exceed the capabilities of conventional machines. As of November 2025, the El Capitan supercomputer at Lawrence Livermore National Laboratory holds the top position on the TOP500 list, achieving a measured performance of 1.809 exaFLOPS on the HPL benchmark (theoretical peak of 2.821 exaFLOPS), enabling breakthroughs in fields like nuclear stockpile stewardship and climate modeling. These systems, often comprising thousands of interconnected nodes, are primarily used for weather forecasting, astrophysics research, and drug discovery, where their ability to perform trillions of floating-point operations per second provides critical insights into large-scale phenomena. Mainframes are enterprise-scale computers optimized for high-volume and in mission-critical environments, prioritizing reliability and input/output throughput over raw speed. IBM's zSystems, for instance, feature specialized architectures with massive caching and instruction sets designed to handle workloads like banking transactions and airline reservations, supporting up to 64 terabytes of per . These machines emphasize through redundant components and , allowing a single mainframe to replace clusters of smaller servers while maintaining 99.999% uptime for global financial operations. Servers form the backbone of modern data centers, scaled for hosting web services, cloud computing, and distributed applications, with designs focused on modularity and energy efficiency in rack-mounted configurations. Hyperscale servers operated by providers like Amazon Web Services (AWS) and Google Cloud, which by 2025 account for nearly half of global data center capacity, enable virtualized environments that support millions of users through technologies like containerization and load balancing. These systems facilitate services such as streaming media, e-commerce, and AI training, with AWS's EC2 instances exemplifying scalable compute resources that dynamically allocate processing based on demand. Purpose-specific computers, including embedded systems, are compact and tailored for integration into devices or machinery, performing dedicated tasks with minimal user interaction and high . In consumer appliances, embedded controllers manage functions like regulation in refrigerators or cycle optimization in washing machines, using microprocessors to ensure reliable operation within power constraints. Automotive electronic control units (ECUs) exemplify this category, processing sensor data in real-time to control engine performance, braking systems, and advanced driver-assistance features, often comprising networks of 50 to 100 ECUs per vehicle for enhanced safety and . Industrial programmable logic controllers (PLCs) serve as ruggedized computers for factory , executing programs to monitor inputs from sensors and control outputs to motors or valves, thereby streamlining processes with deterministic response times under harsh conditions.

By Form Factor and Mobility

Personal computers encompass a range of form factors designed for individual use, primarily desktops and all-in-one systems that prioritize stationary setups with modular components for upgrades and maintenance. Desktop computers, introduced in the early 1980s, typically feature a tower case housing the motherboard, power supply, and peripherals, allowing for easy expansion such as additional storage or graphics cards. The IBM Personal Computer (PC), released in 1981, popularized this design with its open architecture, enabling third-party compatibility and widespread adoption in homes and offices. By the mid-1980s, tower configurations became standard for their vertical orientation, improving space efficiency and airflow in professional environments. All-in-one computers integrate the display and processing unit into a single chassis, reducing desk clutter while maintaining desktop-level performance. Apple's iMac, launched in 1998, exemplified this form factor with its translucent, colorful design and built-in components, reviving consumer interest in personal computing by emphasizing aesthetics and simplicity. Laptops and notebooks represent a shift toward portable computing, balancing power with mobility for on-the-go productivity. The Osborne 1, released in by , was the first commercially successful , weighing 24 pounds and including a keyboard, display, and floppy drives in a luggable case, though its small 5-inch screen limited practicality. Advancements in microprocessors, starting with the in 1971, dramatically reduced size and power consumption, enabling the evolution from bulky portables to slim laptops. By the 2020s, ultrabooks—thin, lightweight laptops defined by Intel's standards—incorporate solid-state drives (SSDs) for faster boot times and storage, along with touchscreen interfaces for intuitive interaction, often featuring processors like Ultra series for extended battery life up to 18 hours. Mobile devices extend computing into pocket-sized form factors, transforming and tablets into versatile personal tools. The , introduced in 1994, is recognized as the first , combining cellular telephony with PDA features like , , and a interface in a brick-like device weighing about 1 pound. Apple's , unveiled in 2007, revolutionized the category with its capacitive screen, app ecosystem, and integration of phone, player, and device, setting the standard for modern smartphones. Tablets, such as the released in 2010, offer larger for media consumption and light productivity, with the original model featuring a 9.7-inch display and up to 64 GB storage, bridging the gap between smartphones and laptops. Wearables push mobility further by integrating computing into body-worn devices for health monitoring, notifications, and augmented interactions. The , first available in 2015, functions as a wrist-worn computer with a square display, heart rate , and integration, syncing with smartphones for calls, apps, and fitness tracking. (AR) glasses, such as Meta's Ray-Ban Meta smart glasses with display announced in 2025, incorporate heads-up displays and AI-driven interfaces for overlaying digital information onto the real world, with integrations allowing wireless connection to computers for virtual desktops and mixed-reality experiences.

Specialized and Unconventional Designs

Neuromorphic computing draws inspiration from the structure and function of biological neural networks to create energy-efficient processors that mimic brain-like processing. These systems employ , where information is encoded in discrete spikes rather than continuous values, enabling low-power operation for tasks like and . A seminal example is IBM's TrueNorth chip, unveiled in 2014, which integrates 1 million neurons and 256 million synapses on a single 28nm die, consuming just 70 mW while supporting asynchronous, event-driven computation. Similarly, Intel's Loihi chip, introduced in 2017, features 128 neuromorphic cores with on-chip learning capabilities, fabricated in a 14nm process to model up to 130,000 neurons, emphasizing adaptability for real-time AI applications through local . Optical computing represents a paradigm shift by leveraging photons instead of electrons for data processing, potentially offering higher speeds and lower heat dissipation due to light's massless nature and minimal interference in transmission. In this approach, optical components like waveguides, modulators, and photodetectors perform logic operations, addressing limitations of electron-based systems such as bandwidth constraints and energy loss. Prototypes in the 2020s include photonic integrated circuits developed by Xanadu, which demonstrate scalable light-based computation using squeezed light states on silicon chips to achieve fault-tolerant operations, paving the way for modular quantum-enhanced systems. Another advancement is MIT's system from 2023, which hybridizes photonic and electronic elements to execute complex algorithms at speeds comparable to electronic processors while reducing power by integrating light for analog computations. DNA and molecular computing exploit the massive parallelism inherent in biochemical reactions to solve computationally intensive problems, using strands of DNA or other molecules as storage and processing media. In a groundbreaking 1994 experiment, encoded a seven-vertex into DNA molecules and used chain reactions to generate all possible paths, selectively amplifying those satisfying the —a NP-complete challenge—demonstrating molecular-scale in a . Contemporary lab-scale implementations build on this by harnessing DNA's ability to perform billions of operations simultaneously through hybridization and enzymatic processes, though scalability remains limited by error rates in synthesis and readout, confining applications to optimization and proofs-of-concept. Memristor-based designs incorporate resistive memory elements that retain conductance states analogous to synaptic weights, enabling compact, non-volatile hardware for neuromorphic and analog . Hewlett-Packard Labs pioneered practical memristors in the late 2000s, fabricating nanoscale devices from that exhibit in current-voltage characteristics, allowing without power. In the , HP integrated these into crossbar arrays for brain-inspired systems, where memristors simulate analog neural dynamics with low overhead, as shown in prototypes supporting in-memory computation to reduce data movement bottlenecks in traditional von Neumann architectures. This approach enhances efficiency in edge AI by mimicking biological plasticity, with devices switching resistance states to store and process weights locally.

Hardware Components

Central Processing and Control Units

The (CPU), often regarded as the brain of a computer, is the primary component responsible for executing instructions from programs by performing the basic operations of fetch, decode, and execute. This architecture fundamentally follows the von Neumann model, where instructions and data share a common bus, leading to the Von Neumann bottleneck that limits performance due to constraints. In this design, the CPU interacts with to retrieve instructions, processes them through its internal units, and stores results, enabling the stored-program concept where both code and data reside in the same addressable space. At the heart of the CPU lies the , which orchestrates the execution of instructions by managing the fetch-decode-execute cycle. It fetches the next instruction from using the , decodes it to determine the required operation—often via that translates high-level instructions into simpler control signals—and then directs the appropriate hardware components to execute it, followed by writing back results if needed. This process typically involves a pipelined structure with stages such as instruction fetch, decode, execute, access, and write-back, allowing overlapping operations to improve throughput, as pioneered in designs like the System/360. , implemented as in , provides flexibility for handling complex instructions without altering hardware, a technique refined in modern processors like those from . The (ALU) serves as the computational core within the CPU, executing arithmetic and logical operations on . For arithmetic tasks, it performs operations such as , where two operands A and B yield sum S via binary addition with carry propagation, or subtraction using representation. Logical operations include bitwise , and XOR, which manipulate bits for tasks like masking or conditional branching, while status flags (e.g., zero, carry, overflow) are set based on results to influence decisions. These units operate on fixed-width data paths, typically 32 or 64 bits in contemporary designs, ensuring efficient handling of and floating-point computations through dedicated circuits. Supporting these operations are key internal components like registers and cache hierarchies, which enhance speed and efficiency. Registers, such as the accumulator, index registers, and , provide ultra-fast, on-chip storage for immediate data access during execution, holding operands and intermediate results. Cache memory, organized in levels—L1 for smallest and fastest access (typically 32-64 KB per core), L2 for moderate capacity (256 KB to 1 MB), and L3 for shared larger pools (up to 128 MB across cores)—stores frequently used data closer to the CPU to mitigate latency from main , reducing average access times from hundreds of cycles to just a few. This hierarchy, informed by principles of , significantly boosts performance in real-world workloads. Modern CPUs incorporate advanced enhancements to overcome classical limitations, including multi-core designs and . Multi-core processors, such as AMD's series, integrate multiple independent processing cores on a single chip—by 2025 models like the 9 featuring 16 or more cores—to enable parallel execution of threads, dramatically improving multitasking and compute-intensive applications like . allows the CPU to dynamically reorder instructions for completion as soon as dependencies are resolved, bypassing stalls from data hazards and increasing , a technique central to high-performance architectures since its implementation in the . These innovations, combined with superscalar designs that issue multiple instructions per cycle, have driven exponential performance gains, with clock speeds stabilizing around 3-5 GHz while core counts and efficiency metrics advance.

Memory and Storage Systems

Memory and storage systems in computers form a designed to balance speed, capacity, and cost, enabling efficient data access during processing. At the top of this hierarchy are CPU registers, which provide the fastest access times—typically in the range of 0.5 to 1 —and store immediate data for the (CPU), such as operands for arithmetic operations. Below registers lies primary memory, primarily implemented as (RAM), which serves as the main working storage for active programs and data. Primary memory, or RAM, is volatile, meaning it loses all stored when power is removed, unlike non-volatile secondary storage. It consists mainly of dynamic RAM (DRAM) for bulk storage and static RAM (SRAM) for smaller, faster components. DRAM uses capacitors to store bits, requiring periodic refreshing to maintain , with typical access times of 50-60 nanoseconds. In contrast, SRAM employs flip-flop circuits that do not need refreshing, achieving faster access times of about 10 nanoseconds, though at higher cost and lower density, making it suitable for limited high-speed applications. To bridge the significant speed gap between the CPU's nanosecond-scale requirements and DRAM's slower access, computers employ multi-level cache , typically organized into L1, L2, and L3 caches. L1 cache, closest to the CPU cores, offers the fastest access (around 1-4 nanoseconds) but smallest capacity (e.g., 32-64 KB per core); L2 provides larger size (256 KB to a few MB) with slightly higher latency (4-10 nanoseconds); and L3 serves multiple cores with even greater capacity (several MB to tens of MB) but access times of 10-20 nanoseconds or more. Cache organization uses mapping techniques like direct-mapped, where each block maps to exactly one cache line for simplicity and speed; fully associative, allowing any block to map anywhere but requiring complex searches; and set-associative, a dividing the cache into sets of lines (e.g., 2-way or 4-way) to balance performance and hardware overhead. For persistent data storage beyond volatile primary memory, secondary storage devices retain information without power. Hard disk drives (HDDs) use rotating magnetic platters coated with ferromagnetic material, where read/write heads access data sectors; platters typically spin at 5,400 to 15,000 RPM, resulting in seek times of several milliseconds (e.g., average 4-9 ms) due to mechanical movement. Solid-state drives (SSDs) have become dominant in consumer applications since the 2010s and are increasingly adopted in enterprise for performance-critical tasks, comprising a significant portion of shipments by 2025; they employ NAND flash memory cells that store charge in floating-gate transistors for non-volatile operation, offering much faster random access (tens of microseconds) without moving parts. Emerging technologies, such as Compute Express Link (CXL) memory, continue to explore ways to enhance persistent memory performance and coherence in disaggregated systems as of 2025.

Input and Output Devices

Input and output devices, often referred to as peripherals, enable users to interact with computers by entering data and commands or receiving feedback through visual, auditory, or tactile means. These devices bridge the gap between users and digital systems, facilitating tasks from text entry to presentation. Early computers relied on punched cards or switches for input and teletypewriters for output, but modern peripherals have evolved into intuitive, high-speed interfaces that support diverse applications.

Input Devices

Keyboards remain the primary input method for text and command entry, with the layout originating in the 1870s as a mechanical design by to prevent key jams by separating common letter pairs. Modern computer keyboards adapt this layout with ergonomic features, membrane or mechanical switches, and programmable keys for enhanced productivity. The , invented by in 1964 at Stanford Research Institute, introduced pointing and clicking as a paradigm, using a wooden prototype with perpendicular wheels to track movement on a surface. This device revolutionized navigation, evolving from mechanical rollers to optical sensors by the for precise cursor control. Touchscreens provide direct interaction via finger or stylus gestures. Capacitive touch technology was first developed in 1965 by E.A. Johnson, with an early transparent capacitive touchscreen created in 1973 by engineers Frank Beck and Bent Stumpe at CERN for controlling particle accelerator interfaces. Capacitive touch detects electrical changes from skin contact, enabling multi-touch capabilities like pinch-to-zoom, which became widespread in smartphones and tablets after Apple's 2007 iPhone integration. Sensors such as cameras capture visual input for applications like facial recognition or video conferencing, while microphones convert sound waves into digital signals for voice commands and audio recording, supporting real-time processing in virtual assistants.

Output Devices

Displays output visual information, transitioning from cathode-ray tube (CRT) technology in the mid-20th century—which used electron beams to illuminate phosphors for monochrome or color images—to liquid crystal displays (LCDs) in the for thinner, energy-efficient panels. Organic (OLED) displays, emerging in the 2000s, offer superior contrast and flexibility by self-emitting light from organic compounds, with resolutions reaching 8K (7680×4320 pixels) by 2025 for immersive experiences in professional and consumer monitors. Printers produce hard copies, with inkjet models tracing back to continuous inkjet experiments in the and becoming consumer viable in the through thermal bubble-jet mechanisms that eject precise ink droplets for . Laser printers, introduced commercially by in 1984, use electrophotographic processes to fuse toner onto paper, achieving high-speed, high-resolution output suitable for office documents. Speakers deliver audio output, building on dynamic driver principles from the where voice coils in magnetic fields vibrate diaphragms to produce sound waves; computer-specific speakers integrated with PCs since the via sound cards for stereo playback in applications.

I/O Interfaces

Standardized interfaces ensure reliable data exchange between peripherals and computers. The Universal Serial Bus (USB), introduced in 1996 by a including and , unified connections for keyboards, mice, and storage with plug-and-play functionality, evolving from USB 1.1's 12 Mbps speeds to USB 4.0's 40 Gbps by 2019, supporting video and power delivery up to 100W in 2025 implementations. (High-Definition Multimedia Interface), launched in 2002 by promoters like and , transmits uncompressed audio and video over a single cable, succeeding analog standards with support for up to 8K resolutions and features like Ethernet and 3D in later versions.

Accessibility Features

Accessibility-focused devices enhance usability for users with disabilities. Braille displays convert digital text into tactile output using piezoelectric pins that form refreshable cells, typically 20 to 80 characters wide, syncing with screen readers for real-time navigation on computers and smartphones. Voice recognition systems, such as Apple's introduced in 2011, integrate with devices to interpret spoken commands for hands-free operation, supporting tasks like dictation and app control while adapting to accents and integrating with accessibility tools like for blind users. These peripherals, often controlled via the CPU's interrupt-driven I/O mechanisms, ensure inclusive interaction without altering core system architecture.

Interconnects and Expansion

Interconnects in computers facilitate the transfer of , , and control signals between components such as the CPU, , and peripherals, enabling seamless hardware communication within the system. These connections are primarily handled through buses, which consist of parallel lines divided into address buses for specifying locations, buses for carrying actual information, and control buses for managing timing and operations. Address buses are unidirectional, directing to or from specific locations, while buses are bidirectional to support both reading and writing. Modern buses have evolved to support high-speed data transfer, with (PCIe) serving as a dominant standard for internal connectivity. The PCIe 5.0 specification, finalized in May 2019, achieves data rates of 32 GT/s per lane, doubling the bandwidth of its predecessor and enabling faster communication for demanding applications. By 2025, PCIe 5.0 has become widely adopted in high-performance systems, supporting configurations up to 128 lanes for enhanced throughput. For universal peripheral connections, provides a versatile port standard, allowing simultaneous data transfer, video output, and power delivery through a single reversible connector. Expansion slots allow users to add or upgrade hardware components, evolving from earlier standards like the (AGP), introduced in 1996 specifically for graphics cards to accelerate . AGP offered higher bandwidth than PCI but was superseded by PCIe around 2004, which provides scalable lanes and greater flexibility for modern GPUs and other add-in cards. Motherboards integrate these slots via chipsets, such as Intel's Z-series (e.g., Z790 and Z890), which manage PCIe lanes, , and I/O routing to support high-end configurations. Wireless interconnects complement wired buses by enabling cable-free connections for peripherals and short-range networking. , first specified in 1999, operates on the 2.4 GHz band for low-power, short-range data exchange between devices like keyboards and . , based on IEEE 802.11ax (), ratified in 2021, delivers up to 9.6 Gbit/s throughput with improved efficiency in dense environments, making it a standard for intra-system wireless expansion by 2025. Power delivery through interconnects has scaled with component demands, adhering to the standard established in the mid-1990s for desktop power supplies, which provides regulated DC voltages via a 24-pin connector. The rise of AI accelerators, such as NVIDIA's H100 GPU requiring up to 700W per unit, has driven PSU capacities beyond 1000W to handle multi-GPU setups and transient power spikes. These mechanisms connect input/output devices like displays and storage, ensuring reliable system operation.

Software Fundamentals

Operating Systems and Firmware

An operating system (OS) is that manages hardware resources and provides services for computer programs, acting as an intermediary to abstract hardware complexities and enable efficient . Core functions include process management, where the OS schedules multiple processes to share the CPU; common algorithms include , which allocates fixed time slices to processes in a cyclic manner to ensure fairness in environments, and priority scheduling, which assigns higher priority to critical processes to meet deadlines or user needs. is another key function, implementing through paging, which divides physical memory into fixed-size pages and maps virtual addresses to physical ones, allowing processes to use more memory than physically available by swapping pages to disk. Major types of operating systems include Unix-like systems, which originated from the 1970s but saw significant evolution with the Linux kernel, first released by Linus Torvalds in 1991 as a free, open-source alternative inspired by Minix. Linux powers numerous distributions, such as Ubuntu, launched in 2004 by Canonical Ltd. and widely adopted by 2025 for desktops, servers, and cloud environments due to its stability and community support. Microsoft's Windows family relies on the NT kernel, introduced with Windows NT 3.1 in 1993, featuring a hybrid architecture that supports multitasking, security, and compatibility across consumer and enterprise versions. Apple's macOS is built on the Darwin operating system, released open-source in 2000, with its XNU hybrid kernel combining Mach microkernel, BSD components, and Apple extensions for performance and security on Apple hardware. Firmware, such as (Basic Input/Output System) and its successor (Unified Extensible Firmware Interface), consists of low-level software embedded in hardware to initialize components and facilitate the boot process by loading the OS from storage. , developed in the 1970s and standardized by for PCs, performs power-on self-tests and basic hardware setup before handing control to the bootloader. , specified by the UEFI Forum starting in 2005, extends capabilities with support for larger disk partitions, faster boot times, and modular drivers, while introducing Secure Boot in the 2.3.1 specification of 2011 to cryptographically verify the integrity of bootloaders and OS images, preventing from loading during startup. Real-time operating systems (RTOS) are specialized OS variants designed for embedded systems requiring predictable, deterministic responses to events within strict time constraints, unlike general-purpose OS that prioritize throughput. , an open-source RTOS kernel first released in 2003, is widely used in IoT devices and microcontrollers for its small footprint, support for over 40 architectures, and features like preemptive multitasking, making it suitable for applications in , automotive controls, and industrial automation.

Programming Languages and Paradigms

Programming languages serve as formalized means for humans to express computations and instructions that computers can execute, evolving from low-level representations tied closely to hardware to high-level abstractions that prioritize and . These languages enable the stored-program concept, where instructions and data reside in memory and are processed uniformly by the . The design of a programming language influences its suitability for specific domains, such as scientific computation, , or , while paradigms define the underlying approach to structuring code and managing program state. At the lowest level, consists of binary instructions—sequences of 0s and 1s—that directly control the computer's hardware, typically comprising an specifying the operation and operands providing data or addresses. Assembly languages offer a symbolic, human-readable alternative to pure , using mnemonics (e.g., MOV for move in x86 assembly) that assemblers translate into binary equivalents, facilitating direct hardware manipulation while remaining architecture-specific. For instance, x86 assembly, developed by in the 1970s, remains influential in low-level due to its fine-grained control over processor resources. High-level programming languages abstract away hardware details, allowing developers to write code closer to natural language or mathematical notation, which compilers or interpreters then translate into machine code. Fortran, introduced in 1957 by John Backus and a team at IBM, was the first widely adopted high-level language, optimized for scientific and engineering computations with features like array operations and loop constructs. COBOL, specified in 1960 through the Conference on Data Systems Languages (CODASYL) under the influence of Grace Hopper, targeted business data processing with English-like syntax for records and reports, enabling non-technical users to contribute to programming efforts. C, developed by Dennis Ritchie at Bell Labs in 1972, became a cornerstone for systems and embedded programming due to its efficiency and portability, influencing countless subsequent languages through its procedural style and memory management primitives. In modern contexts, Python, created by Guido van Rossum in 1991 at Centrum Wiskunde & Informatica, exemplifies versatility across scripting, data analysis, and web development, owing to its simple syntax and extensive libraries. Programming paradigms represent distinct methodologies for organizing code and solving problems, each emphasizing different principles of computation. The imperative paradigm, foundational to many languages, focuses on explicitly describing sequences of commands that modify program state, often through procedural constructs like loops and conditionals, as seen in C's step-by-step execution model. The object-oriented paradigm structures software around objects that encapsulate and behavior, supporting concepts like classes, inheritance, and polymorphism; , designed by at in 1995, popularized this approach for platform-independent applications via its "" bytecode model. The functional paradigm treats computation as the evaluation of mathematical functions, emphasizing immutability, pure functions without side effects, and higher-order functions; , standardized in 1990 by a committee including , exemplifies this by enforcing and , aiding in concurrent and reliable . Languages are executed through two primary mechanisms: compilation, where is translated entirely into prior to runtime for efficient execution, as in C compilers producing native binaries; or interpretation, where code is read and executed line-by-line at runtime, offering flexibility but potentially slower performance, as in Python's bytecode interpreter. Many contemporary languages blend these via , dynamically optimizing code during execution; Google's , released in 2008 for Chrome and later powering , employs JIT to compile to native code on-the-fly, dramatically improving speeds by adapting to runtime patterns.

Applications and User Interfaces

Applications software encompasses a wide range of programs designed for end-user tasks, transforming computers into versatile tools for productivity, entertainment, and information access. Word processors, such as , which was first released in 1983 for systems, enable users to create, edit, and format documents with features like spell-checking and layout tools, revolutionizing office work by replacing typewriters. Web browsers like NCSA Mosaic, introduced in 1993, popularized graphical navigation of the internet by rendering text and images seamlessly, making the accessible to non-technical users. Similarly, video games such as Doom, released in 1993 by , demonstrated immersive 3D environments and multiplayer capabilities, establishing first-person shooters as a major genre and influencing gaming hardware demands. Graphical user interfaces (GUIs) have fundamentally shaped human-computer interaction by providing intuitive visual elements over command-line inputs. The , developed in 1973 at PARC, pioneered the GUI with a , mouse-driven windows, and icons, influencing subsequent designs despite limited commercial deployment. Microsoft's , launched in 1985, brought tiled windows and a mouse interface to PCs, building on these ideas to create a multitasking environment atop . Apple's Aqua interface, unveiled in 2000 with Mac OS X and fully integrated in its 2001 release, introduced translucent elements, smooth animations, and a liquid-like aesthetic to enhance visual appeal and usability on Macintosh systems. Touch-based UIs emerged prominently with in 2007, leveraging gestures on the to enable direct manipulation of on-screen objects, paving the way for paradigms. Web applications extend traditional software by running in browsers, leveraging the , CSS, and stack standardized by the W3C to deliver dynamic, cross-platform experiences without native installation. These technologies allow for responsive layouts, interactive elements, and client-side processing, powering everything from clients to collaborative tools. Progressive Web Apps (PWAs), conceptualized by in 2015, enhance this model with service workers for offline functionality, app-like installation prompts, and push notifications, bridging the gap between web and native apps while maintaining web openness. Accessibility features ensure inclusive interaction for users with disabilities, integrating seamlessly into applications and interfaces. Screen readers like JAWS, released in by Freedom Scientific, convert visual content into synthesized speech or , supporting navigation via keyboard shortcuts and attributes for complex web elements. High-contrast modes, available in operating systems since , adjust color schemes to boost visibility for low-vision users by emphasizing edges and text with bold contrasts, often toggled via . These tools, often built atop OS windowing systems, promote equitable access without altering core functionality.

Software Development and Maintenance

Software development encompasses a range of methodologies designed to structure the creation of reliable and efficient programs. The , introduced by in his 1970 paper "Managing the Development of Large Software Systems," represents a sequential approach where each phase—such as , , , verification, and —must be completed before the next begins. This linear process was particularly suited to projects with well-defined requirements, emphasizing documentation and predictability in large-scale systems. In response to the rigidities of , Agile methodologies emerged to promote flexibility and iterative progress. The Agile Manifesto, authored by seventeen software practitioners in 2001, outlines four core values: individuals and interactions over processes and tools, working software over comprehensive documentation, customer collaboration over contract negotiation, and responding to change over following a plan. Supported by twelve principles, Agile fosters adaptive planning, early delivery, and continuous improvement through practices like Scrum and , enabling teams to address evolving needs in dynamic environments. Building on Agile, integrates development and operations to enhance collaboration and automate workflows, originating from discussions led by Patrick Debois in 2009 at the first DevOpsDays conference. This approach, popularized in the 2010s, emphasizes , delivery, and deployment, reducing silos between teams and accelerating software release cycles while maintaining quality. Testing is integral to , conducted at multiple levels to verify functionality and catch defects early. examines individual components, such as functions or methods, in isolation; assesses interactions between modules; and evaluates the complete, integrated application against specified requirements. A seminal tool for in is , developed by and in 1997, which automates test execution and reporting to support . Bugs, or defects in software, commonly include syntax errors—which violate language rules and prevent compilation—and logic errors, where code executes but produces incorrect results due to flawed algorithms or assumptions. Debugging involves systematic identification and resolution of these issues, often facilitated by version control systems like , created by in 2005 to track changes in the codebase and enable collaborative branching and merging. Open-source models, exemplified by the GNU General Public License (GPL) drafted by in 1989, promote community-driven debugging and maintenance by requiring derivative works to remain freely modifiable and distributable. Software maintenance ensures ongoing reliability post-deployment, involving corrective updates for bugs, adaptive changes for new environments, and preventive enhancements for future-proofing. Patches address vulnerabilities swiftly, while legacy code—outdated systems hard to modify—poses challenges, as seen in the Y2K problem, where two-digit year representations risked widespread failures at the 2000 millennium transition, prompting global remediation efforts estimated at $300–$600 billion. By 2025, emerging threats to current encryption algorithms, such as RSA, necessitate migration to ; the National Institute of Standards and Technology (NIST) finalized initial standards in 2024 to mitigate risks from quantum attacks that could decrypt legacy data.

Networking and Applications

Computer Networks and Protocols

Computer networks enable the interconnection of multiple computing devices to facilitate data exchange, resource sharing, and communication. These networks are structured according to specific topologies that define how devices are physically or logically arranged, influencing performance, reliability, and scalability. Local Area Networks (LANs) typically span a limited geographic area, such as a single building or campus, and utilize high-speed, low-latency connections for efficient local communication. In contrast, Wide Area Networks (WANs) connect LANs across larger distances, often spanning cities or countries, and rely on lower-speed links like leased lines or public infrastructure to enable broader connectivity. Common network topologies include bus, , and configurations. In a bus topology, all devices connect to a single central cable, allowing transmission along the shared medium, though it is susceptible to failures if the cable is damaged. A topology connects each device to a central hub or switch, providing easier fault isolation and but depending on the central node's reliability. topologies, where devices interconnect with multiple others, offer high redundancy and , making them suitable for critical applications, albeit at the cost of increased cabling complexity. Protocols govern the rules and formats for data transmission in these networks, ensuring reliable and standardized communication. The Open Systems Interconnection (, developed by the (ISO), provides a dividing network functions into seven layers: physical (layer 1, handling bit transmission), data link (layer 2, node-to-node delivery), network (layer 3, routing), transport (layer 4, end-to-end reliability), session (layer 5, dialog control), presentation (layer 6, data formatting), and application (layer 7, user interfaces). This layered approach allows independent development and interoperability across diverse systems. The TCP/IP suite, foundational to modern networking, emerged in the 1970s as part of the ARPANET project, replacing earlier protocols with a more robust set for internetworking. It comprises the Transmission Control Protocol (TCP) for reliable, connection-oriented delivery and the Internet Protocol (IP) for addressing and routing, forming the core of the Internet protocol stack. Ethernet, standardized as IEEE 802.3 in 1983 based on a 1980 specification by Xerox, DEC, and Intel, serves as a primary wired LAN protocol using carrier-sense multiple access with collision detection (CSMA/CD) for medium access. Addressing in networks relies on unique identifiers for devices and hosts. Media Access Control (MAC) addresses, 48-bit identifiers assigned by the IEEE, operate at the to uniquely identify network interfaces within a LAN. IP addresses provide logical host identification: IPv4, defined in RFC 791 (1981), uses 32-bit addresses (e.g., 192.0.2.1) supporting about 4.3 billion unique hosts, while , specified in RFC 4291 (2006), employs 128-bit addresses to accommodate vastly more devices amid IPv4 exhaustion. Basic network security measures protect against unauthorized access and data breaches. Firewalls act as barriers between trusted internal networks and untrusted external ones, inspecting and filtering traffic based on predefined rules, often at the network or transport layers. secures data in transit using algorithms like the (AES), a symmetric standardized by NIST in FIPS 197 (2001), supporting key sizes of 128, 192, or 256 bits for confidential communication. Virtual Private Networks (VPNs), popularized in the , create secure tunnels over public networks using protocols like (initially specified in RFC 2401, 1998) to encapsulate and encrypt traffic, enabling private connectivity as if on a local network.

The Internet and Web Technologies

The originated as a packet-switched network developed by the United States Department of Defense's Advanced Research Projects Agency (), with establishing its first connections between four university computer nodes in late 1969. This experimental network laid the groundwork for interconnecting disparate computer systems, evolving through the adoption of the TCP/IP protocol suite in 1983, which standardized data transmission across networks. By 1985, the (NSF) launched NSFNET to connect supercomputing centers and academic institutions, expanding access beyond military applications and forming the backbone of the modern . The World Wide Web (WWW), a key application layer on the Internet, was invented by British computer scientist Tim Berners-Lee while working at CERN in 1989, with its foundational proposal outlining a hypertext system for information sharing among researchers. Development continued through 1990–1991, culminating in the first web server and browser, utilizing Hypertext Transfer Protocol (HTTP) for communication and Hypertext Markup Language (HTML) for structuring content, enabling the creation of interconnected documents accessible via uniform resource locators (URLs). The Web's evolution progressed to Web 2.0 around 2004, coined during a conference organized by Tim O'Reilly and Dale Dougherty, emphasizing user-generated content, interactivity, and social collaboration through platforms like blogs and wikis. By 2025, Web 3.0 has emerged as a vision for a decentralized Web, leveraging blockchain technologies for peer-to-peer interactions, ownership of digital assets via tokens, and reduced reliance on centralized intermediaries, though adoption remains in early stages with ongoing scalability challenges. Core technologies supporting the and Web include the (DNS), invented by in 1983 through RFC 882 and RFC 883, which translates human-readable domain names into numerical IP addresses for routing. Web browsers revolutionized access, with Netscape Navigator 1.0 released on December 15, 1994, as the first commercial browser supporting images and forms, accelerating the Web's popularity. Application Programming Interfaces (APIs) and the Representational State Transfer (REST) architectural style, introduced by in his 2000 dissertation, further enabled scalable web services by defining stateless, resource-oriented interactions over HTTP. These innovations, building on TCP/IP protocols, have facilitated seamless data exchange. As of 2025, the connects approximately 6 billion users worldwide, representing about 75% of the global population, according to estimates from the (ITU), with growth driven by mobile access in developing regions. adoption has reached approximately 45% of global traffic to major sites like , addressing and enabling expanded connectivity for devices. These statistics underscore the Internet's scale, though disparities persist, with higher penetration in high-income countries exceeding 90%.

Distributed Systems and Cloud Computing

Distributed systems involve and spread across multiple interconnected machines, enabling , , and resource sharing beyond the capabilities of single computers. This paradigm emerged to address the limitations of , particularly as networks grew in the late . Early distributed models focused on coordinating tasks between clients and servers or among peers, while modern extensions like and leverage and high-speed networks to handle massive-scale operations. The client-server model, a foundational architecture, originated in the 1970s through protocols such as and the initial (FTP). , specified in RFC 97 in , allowed remote terminal access from client hosts to server systems, establishing a clear separation where clients initiated requests and servers provided responses. Similarly, FTP's early specification in RFC 114 () enabled file transfers between hosts, formalizing the asymmetric roles that defined client-server interactions for resource access over networks. This model dominated enterprise and internet applications by the 1980s, powering services like and web browsing, though it centralized control on servers, creating single points of failure. In contrast, (P2P) models decentralize roles, allowing nodes to act as both clients and servers for direct resource sharing. , launched on June 1, 1999, by and , popularized P2P through its file-sharing application, which used a hybrid architecture: central servers indexed files while users exchanged data directly, achieving rapid with millions of users by 2000. Despite legal challenges leading to its shutdown in 2001, demonstrated P2P's potential for distributed content delivery, influencing subsequent systems like . P2P networks reduce reliance on central infrastructure but introduce challenges in coordination and security. Cloud computing builds on distributed principles by providing on-demand access to shared computing resources over the , often abstracted through service models. , a key enabler, allows multiple virtual machines to run on a single physical host, isolating workloads for efficiency. , released in 1999, introduced commercially, using to execute guest operating systems unmodified on standard PCs, as detailed in its foundational technical overview. This technology underpinned cloud scalability by enabling resource pooling. The primary cloud service models are (IaaS), (PaaS), and (SaaS). IaaS offers virtualized computing resources like servers and storage, with (EC2) launching on August 25, 2006, as the first major public IaaS, allowing users to rent resizable compute capacity without managing hardware. PaaS provides development platforms including runtime environments and databases, abstracting infrastructure management for faster application deployment. SaaS delivers fully managed software applications accessible via the web, such as or CRM tools, shifting focus from installation to subscription-based use. These models, formalized in NIST standards, promote elasticity but require robust underlying distributed infrastructure. Handling in distributed systems relies on frameworks like Hadoop, which implements for parallel processing. , introduced by in a 2004 paper, simplifies large-scale data analysis by dividing tasks into map (processing input) and reduce (aggregating outputs) phases across clusters, automatically handling fault tolerance and load balancing. Hadoop, created by in 2006 as an open-source project derived from 's technologies, adopted alongside the Hadoop Distributed File System (HDFS) for fault-tolerant storage, enabling petabyte-scale processing on commodity hardware. Scalability in such systems faces trade-offs outlined by the , proposed by Eric Brewer in 2000 and proven in 2002, which states that distributed systems can guarantee at most two of consistency (all nodes see the same data), (every request receives a response), and partition tolerance (system operates despite network failures). For instance, Hadoop prioritizes and partition tolerance over strict consistency, using in HDFS to manage large-scale replication. Edge computing extends distributed systems by processing data near its generation sources, such as IoT devices, to minimize latency and bandwidth usage. This approach contrasts with centralized models by deploying compute resources at the network periphery, enabling real-time decisions in bandwidth-constrained environments. With networks enabling sub-millisecond latencies by 2025, edge computing supports IoT applications like autonomous vehicles and , where delays could be critical; for example, 's high throughput allows edge nodes to handle data locally, reducing round-trip times from servers by up to 90% in urban deployments. This synergy addresses IoT's explosive growth, with an estimated 21 billion connected devices as of 2025, by offloading non-critical tasks to the while ensuring low-latency operations at the edge.

Emerging Applications in AI and Data

Machine learning, a core component of , encompasses techniques that enable computers to learn patterns from without explicit programming. involves training models on labeled datasets, where inputs are paired with known outputs to predict or classify new , such as in image recognition tasks. , in contrast, analyzes unlabeled to identify hidden structures, like clustering similar customer behaviors in marketing analytics. These approaches form the foundation for scalable AI systems, with frameworks accelerating their implementation. TensorFlow, released by in November 2015, provides a flexible platform for building and deploying models at scale, supporting distributed training across heterogeneous systems. , introduced by Facebook's AI Research lab with its initial stable release in January 2017, emphasizes dynamic computation graphs for rapid prototyping, particularly in research settings. Both frameworks have democratized access to advanced , enabling developers to handle complex neural networks efficiently. In , (GPT) models have revolutionized text generation and understanding. OpenAI's , released in 2018, demonstrated pre-training on large corpora followed by fine-tuning for tasks like . Subsequent iterations, including in 2020 with 175 billion parameters for few-shot learning and in 2023 supporting multimodal inputs, have powered applications from chatbots to . By 2025, these models continue to advance conversational AI while addressing challenges like bias mitigation. Computer vision applications leverage for real-time perception in autonomous vehicles, where convolutional neural networks process camera feeds to detect objects, lanes, and pedestrians. Seminal work includes the use of for end-to-end driving policies, as in NVIDIA's 2016 pilotnet model, which maps raw pixels directly to steering commands. Modern systems, such as those in Tesla's , integrate these techniques for safe navigation, reducing human error in dynamic environments. Big data analytics processes vast datasets to extract insights, with emerging as a unified engine for batch, streaming, and interactive workloads. Originally developed at UC Berkeley's AMPLab and open-sourced in 2010, Spark's resilient distributed datasets enable in-memory computation 100 times faster than Hadoop for iterative algorithms. Data lakes complement this by serving as centralized repositories for storing raw, unstructured data in native formats, allowing flexible schema-on-read querying for analytics. Privacy regulations like the EU's (GDPR), effective May 2018, mandate consent and data minimization to protect personal information in such ecosystems. The Internet of Things (IoT) integrates computing into everyday devices, with an estimated 21.1 billion connected devices worldwide by 2025, facilitating data-driven applications. In smart cities, IoT sensors optimize traffic flow by adjusting signals in real-time, as seen in Singapore's Intelligent Transport System, reducing congestion by up to 15%. For health monitoring, wearable devices like Fitbit track vital signs continuously, enabling remote patient care and early detection of anomalies through cloud-synced analytics. Cloud platforms briefly support these by providing scalable resources for training AI models on IoT-generated data.

Societal Impact and Future Directions

Professions, Education, and Organizations

The field of computing offers diverse professions that drive technological innovation and application. Software engineers focus on designing, coding, and maintaining software systems, often earning an average salary of $140,013 in the United States in 2025. Hardware designers, or computer hardware engineers, create and optimize physical components like processors and circuit boards, with median annual wages reaching $155,020 as of recent data projecting into 2025. Data scientists extract insights from large datasets using statistical and machine learning techniques, commanding average salaries of $128,047 in the US during the same period. Cybersecurity professionals, including analysts and ethical hackers, safeguard networks and data against threats, with median pay at $124,910 annually. Education pathways in computing range from formal degrees to practical training programs. Computer science degrees, typically bachelor's or higher, emphasize foundational topics like algorithms, data structures, and computational theory, with curricula often shaped by influential contributions recognized by the ACM , which honors breakthroughs that inform academic standards since its inception in 1966. Coding bootcamps provide accelerated, job-focused instruction; for example, , established in 2011, offers immersive courses in and data analytics to transition career changers into tech roles. Industry certifications validate specific skills without a full degree—CompTIA's A+ certification covers entry-level IT support and hardware troubleshooting, while Cisco's focuses on network configuration and security fundamentals, both widely recognized for professional advancement. Professional organizations play a central role in advancing computing standards and community. The Association for Computing Machinery (ACM), founded in 1947, supports research, education, and ethical practices through publications, conferences, and awards like the . The Institute of Electrical and Electronics Engineers (IEEE), formed in 1963 via the merger of earlier engineering societies, develops technical standards for computing hardware, software, and communications protocols. Standards bodies such as the (W3C), established in 1994, maintain web technologies like and accessibility guidelines, while the (ISO), dating to 1947, defines global protocols for data interchange and cybersecurity. Efforts to enhance diversity in computing address underrepresentation through targeted initiatives. Pioneers like , who in 1952 completed the first to translate symbolic code into machine language, inspired generations of women in the field by demonstrating practical applications of computing. Programs under the Women in Tech umbrella, including mentorship and conferences, build on such legacies to promote gender equity. As of 2025, women comprise approximately 26% of the US computing workforce, while African American women hold only 3% of related jobs, prompting ongoing interventions for underrepresented groups like Black, Hispanic, and Indigenous professionals to foster .

Ethical, Social, and Environmental Considerations

The proliferation of computing technologies has exacerbated the , where access to computers and the remains unevenly distributed globally. As of 2025, approximately 2.2 billion people—about 27% of the world's population—remain offline, with disparities most pronounced in low-income regions such as and , where limitations and economic barriers hinder connectivity. This gap limits opportunities in , , and civic participation, perpetuating socioeconomic inequalities. Privacy concerns have intensified with the widespread collection and use of in computing systems. The 2018 Cambridge Analytica scandal exemplified these risks, as the firm harvested data from over 87 million users without consent to influence political campaigns, including the 2016 U.S. presidential election, highlighting vulnerabilities in social media platforms and data brokerage practices. Such incidents have spurred regulatory responses, but ongoing challenges persist in balancing innovation with individual rights. Ethical issues in computing prominently include , where automated systems produce unfair outcomes due to flawed training data or design. For instance, facial recognition technologies have demonstrated higher error rates—up to 100 times more likely for Black and Asian faces compared to white faces—in identifying individuals, as revealed in evaluations of commercial algorithms. To address these, frameworks like the EU AI Act, which entered into force in August 2024, classify AI systems by risk levels and impose requirements for transparency, accountability, and bias mitigation in high-risk applications such as hiring and . Environmental impacts of computing are significant, driven by resource-intensive production and operations. Global electronic waste generation reached 62 million metric tons in 2022, equivalent to 7.8 kg per , with projections indicating continued growth to around 82 million tons by 2030 due to shorter device lifecycles and rising consumption. centers, powering services and AI, consumed about 1-2% of global in 2024, contributing to amid surging demand from computational tasks. Efforts in aim to mitigate these effects through energy-efficient hardware and practices. Innovations such as advanced processor chips, including IBM's 2nm technology, enable higher performance with reduced power usage, potentially lowering energy needs by optimizing density and heat management. Surveillance enabled by computing raises ethical dilemmas regarding and power imbalances. Widespread deployment of monitoring technologies, from CCTV to online tracking, amplifies risks of abuse by governments and corporations, often disproportionately affecting marginalized communities. Automation in computing systems poses risks to employment, with studies estimating significant job displacement potential. A seminal 2013 analysis by researchers at the found that 47% of U.S. jobs are at high risk of , particularly in routine manual and cognitive tasks like and , though adoption rates depend on economic and social factors. This underscores the need for reskilling programs to address workforce transitions.

Advances in Computer Architecture

Modern computer architectures continue to evolve through the tension between Reduced Instruction Set Computing (RISC) and Complex Instruction Set Computing (CISC) paradigms, balancing simplicity, power efficiency, and performance. RISC architectures, exemplified by , emphasize a streamlined set of instructions that execute in a single clock cycle, enabling high efficiency in resource-constrained environments. Developed in the by and later commercialized by , the ARM architecture has achieved dominance in by 2025, powering over 95% of smartphones and tablets due to its low-power design and licensing model that allows customization by vendors like Apple and . In contrast, CISC architectures like x86, originating from Intel's 8086 in 1978, support a broader range of complex instructions that can perform multiple operations in one command, historically favoring high-performance desktops and servers but requiring more sophisticated decoding hardware. By 2025, ARM's RISC approach has expanded into data centers and PCs, challenging x86's long-standing supremacy through superior energy efficiency, while hybrid designs incorporate RISC-like microoperations internally to optimize CISC execution. Advancements in parallelism have significantly enhanced computational throughput, with (SIMD) extensions playing a pivotal role in vector processing. NVIDIA's introduction of in November 2006 transformed GPUs into general-purpose accelerators by enabling programmers to leverage SIMD-like (SIMT) execution across thousands of cores, dramatically accelerating tasks like scientific simulations and that exhibit . Multi-threading, which allows concurrent execution of multiple instruction streams on a single processor, has evolved from early implementations in the to widespread adoption in modern CPUs, improving utilization of superscalar pipelines and mitigating latency from accesses. Heterogeneous systems further amplify these gains by integrating CPUs for sequential tasks with GPUs for parallel workloads, as seen in platforms like AMD's and NVIDIA's unified architectures, which by 2025 enable seamless task offloading and boost overall system efficiency for AI and applications. Security in computer architecture has advanced to counter speculative execution vulnerabilities, with mitigations for Spectre and Meltdown—disclosed in January 2018—becoming integral to . These flaws, affecting x86, , and other architectures, exploit branch prediction and caching to leak data across security boundaries, prompting hardware fixes like enhanced barriers and firmware updates that reduce performance overhead to under 5% in most workloads. Trusted Execution Environments (TEEs) provide isolated enclaves for sensitive computations; Intel's (SGX), introduced in 2015 with the Skylake processors, creates hardware-enforced memory regions immune to higher-privilege software attacks, enabling secure cloud processing for applications like . By 2025, TEEs have proliferated across architectures, with TrustZone and similar features ensuring confidentiality in heterogeneous systems. Sustainability drives innovations in low-power designs, addressing the energy demands of scaling densities. TSMC's 2nm (N2) process, entering high-volume in the second half of 2026, utilizes gate-all-around nanosheet s to achieve 10-15% higher performance at iso-power compared to 3nm nodes, alongside 25-30% power reductions that extend battery life in mobile devices and reduce data center cooling needs. These advancements prioritize energy efficiency without sacrificing density, supporting greener computing amid rising counts exceeding 100 billion per chip, and aligning with industry goals for carbon-neutral fabrication by the early .

Prospects in Quantum and Neuromorphic Computing

Quantum computing holds significant promise for surpassing classical limitations through the use of qubits, which leverage superposition and entanglement to perform parallel computations unattainable on traditional hardware. Recent milestones include the development of systems exceeding 1,000 physical qubits, such as IBM's processor with 1,121 qubits announced in 2023, paving the way for scalable architectures by 2025. By 2025, further advancements include IBM's processor scaling toward error-corrected logical qubits, with demonstrations of improved fidelity in multi-qubit gates. Key to reliability is , particularly surface codes, which encode logical qubits into a two-dimensional lattice of physical qubits to detect and correct errors with high thresholds, as demonstrated in experimental implementations achieving error rates below the code's threshold. Applications in optimization, such as , pose a potential threat to RSA by factoring large numbers efficiently, with projections indicating cryptographically relevant systems capable of breaking RSA-2048 could emerge in the 2030s. Neuromorphic computing, inspired by neural structures, advances through event-driven processing that activates computations only upon relevant inputs, enabling efficient edge AI deployments in resource-constrained environments like IoT devices and autonomous systems. This paradigm aims for brain-like , targeting to simulate the brain's approximately 10^{15} synapses while consuming minimal power, potentially reducing use by up to 100 times compared to conventional AI hardware. Despite these prospects, challenges persist, including , where environmental interactions cause qubits to lose coherence rapidly, limiting computation times to milliseconds without advanced isolation techniques. In neuromorphic systems, achieving energy efficiency at scale remains difficult due to fabrication complexities in mimicking and integrating with existing processes. Hybrid classical-quantum approaches are expected to bridge these gaps by 2030, integrating quantum accelerators with classical processors for practical utility-scale applications. Broader impacts include accelerations in , where quantum simulations model molecular interactions with unprecedented accuracy to identify novel compounds faster than classical methods. In modeling, quantum algorithms could enhance predictions by solving complex differential equations for atmospheric dynamics, aiding in more precise forecasting and mitigation strategies.

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