Computing basics
The first computers were used primarily for numerical calculations. However, as any information can be numerically encoded, people soon realized that computers are capable of general-purpose information processing. Their capacity to handle large amounts of data has extended the range and accuracy of weather forecasting. Their speed has allowed them to make decisions about routing telephone connections through a network and to control mechanical systems such as automobiles, nuclear reactors, and robotic surgical tools. They are also cheap enough to be embedded in everyday appliances and to make clothes dryers and rice cookers “smart.” Computers have allowed us to pose and answer questions that could not be pursued before. These questions might be about DNA sequences in genes, patterns of activity in a consumer market, or all the uses of a word in texts that have been stored in a database. Increasingly, computers can also learn and adapt as they operate.
Computers also have limitations, some of which are theoretical. For example, there are undecidable propositions whose truth cannot be determined within a given set of rules, such as the logical structure of a computer. Because no universal algorithmic method can exist to identify such propositions, a computer asked to obtain the truth of such a proposition will (unless forcibly interrupted) continue indefinitely—a condition known as the “halting problem.” (See Turing machine.) Other limitations reflect current technology. Human minds are skilled at recognizing spatial patterns—easily distinguishing among human faces, for instance—but this is a difficult task for computers, which must process information sequentially, rather than grasping details overall at a glance. Another problematic area for computers involves natural language interactions. Because so much common knowledge and contextual information is assumed in ordinary human communication, researchers have yet to solve the problem of providing relevant information to general-purpose natural language programs.
Analog computers
Analog computers use continuous physical magnitudes to represent quantitative information. At first they represented quantities with mechanical components (see differential analyzer and integrator), but after World War II voltages were used; by the 1960s digital computers had largely replaced them. Nonetheless, analog computers, and some hybrid digital-analog systems, continued in use through the 1960s in tasks such as aircraft and spaceflight simulation.
One advantage of analog computation is that it may be relatively simple to design and build an analog computer to solve a single problem. Another advantage is that analog computers can frequently represent and solve a problem in “real time”; that is, the computation proceeds at the same rate as the system being modeled by it. Their main disadvantages are that analog representations are limited in precision—typically a few decimal places but fewer in complex mechanisms—and general-purpose devices are expensive and not easily programmed.
Digital computers
In contrast to analog computers, digital computers represent information in discrete form, generally as sequences of 0s and 1s (binary digits, or bits). The modern era of digital computers began in the late 1930s and early 1940s in the United States, Britain, and Germany. The first devices used switches operated by electromagnets (relays). Their programs were stored on punched paper tape or cards, and they had limited internal data storage. For historical developments, see the section Invention of the modern computer.
Mainframe computer
During the 1950s and ’60s, Unisys (maker of the UNIVAC computer), International Business Machines Corporation (IBM), and other companies made large, expensive computers of increasing power. They were used by major corporations and government research laboratories, typically as the sole computer in the organization. In 1959 the IBM 1401 computer rented for $8,000 per month (early IBM machines were almost always leased rather than sold), and in 1964 the largest IBM S/360 computer cost several million dollars.
These computers came to be called mainframes, though the term did not become common until smaller computers were built. Mainframe computers were characterized by having (for their time) large storage capabilities, fast components, and powerful computational abilities. They were highly reliable, and, because they frequently served vital needs in an organization, they were sometimes designed with redundant components that let them survive partial failures. Because they were complex systems, they were operated by a staff of systems programmers, who alone had access to the computer. Other users submitted “batch jobs” to be run one at a time on the mainframe.
Such systems remain important today, though they are no longer the sole, or even primary, central computing resource of an organization, which will typically have hundreds or thousands of personal computers (PCs). Mainframes now provide high-capacity data storage for Internet servers, or, through time-sharing techniques, they allow hundreds or thousands of users to run programs simultaneously. Because of their current roles, these computers are now called servers rather than mainframes.
Supercomputer
The most powerful computers of the day have typically been called supercomputers. They have historically been very expensive and their use limited to high-priority computations for government-sponsored research, such as nuclear simulations and weather modeling. Today many of the computational techniques of early supercomputers are in common use in PCs. On the other hand, the design of costly, special-purpose processors for supercomputers has been supplanted by the use of large arrays of commodity processors (from several dozen to over 8,000) operating in parallel over a high-speed communications network.
Minicomputer
Although minicomputers date to the early 1950s, the term was introduced in the mid-1960s. Relatively small and inexpensive, minicomputers were typically used in a single department of an organization and often dedicated to one task or shared by a small group. Minicomputers generally had limited computational power, but they had excellent compatibility with various laboratory and industrial devices for collecting and inputting data.
One of the most important manufacturers of minicomputers was Digital Equipment Corporation (DEC) with its Programmed Data Processor (PDP). In 1960 DEC’s PDP-1 sold for $120,000. Five years later its PDP-8 cost $18,000 and became the first widely used minicomputer, with more than 50,000 sold. The DEC PDP-11, introduced in 1970, came in a variety of models, small and cheap enough to control a single manufacturing process and large enough for shared use in university computer centres; more than 650,000 were sold. However, the microcomputer overtook this market in the 1980s.
Microcomputer
A microcomputer is a small computer built around a microprocessor integrated circuit, or chip. Whereas the early minicomputers replaced vacuum tubes with discrete transistors, microcomputers (and later minicomputers as well) used microprocessors that integrated thousands or millions of transistors on a single chip. In 1971 the Intel Corporation produced the first microprocessor, the Intel 4004, which was powerful enough to function as a computer although it was produced for use in a Japanese-made calculator. In 1975 the first personal computer, the Altair, used a successor chip, the Intel 8080 microprocessor. Like minicomputers, early microcomputers had relatively limited storage and data-handling capabilities, but these have grown as storage technology has improved alongside processing power.
In the 1980s it was common to distinguish between microprocessor-based scientific workstations and personal computers. The former used the most powerful microprocessors available and had high-performance colour graphics capabilities costing thousands of dollars. They were used by scientists for computation and data visualization and by engineers for computer-aided engineering. Today the distinction between workstation and PC has virtually vanished, with PCs having the power and display capability of workstations.
Embedded processors
Another class of computer is the embedded processor. These are small computers that use simple microprocessors to control electrical and mechanical functions. They generally do not have to do elaborate computations or be extremely fast, nor do they have to have great “input-output” capability, and so they can be inexpensive. Embedded processors help to control aircraft and industrial automation, and they are common in automobiles and in both large and small household appliances. One particular type, the digital signal processor (DSP), has become as prevalent as the microprocessor. DSPs are used in wireless telephones, digital telephone and cable modems, and some stereo equipment.
Computer hardware
The physical elements of a computer, its hardware, are generally divided into the central processing unit (CPU), main memory (or random-access memory, RAM), and peripherals. The last class encompasses all sorts of input and output (I/O) devices: keyboard, display monitor, printer, disk drives, network connections, scanners, and more.
The CPU and RAM are integrated circuits (ICs)—small silicon wafers, or chips, that contain thousands or millions of transistors that function as electrical switches. In 1965 Gordon Moore, one of the founders of Intel, stated what has become known as Moore’s law: the number of transistors on a chip doubles about every 18 months. Moore suggested that financial constraints would soon cause his law to break down, but it has been remarkably accurate for far longer than he first envisioned. It now appears that technical constraints may finally invalidate Moore’s law, since sometime between 2010 and 2020 transistors would have to consist of only a few atoms each, at which point the laws of quantum physics imply that they would cease to function reliably.
Central processing unit
The CPU provides the circuits that implement the computer’s instruction set—its machine language. It is composed of an arithmetic-logic unit (ALU) and control circuits. The ALU carries out basic arithmetic and logic operations, and the control section determines the sequence of operations, including branch instructions that transfer control from one part of a program to another. Although the main memory was once considered part of the CPU, today it is regarded as separate. The boundaries shift, however, and CPU chips now also contain some high-speed cache memory where data and instructions are temporarily stored for fast access.
The ALU has circuits that add, subtract, multiply, and divide two arithmetic values, as well as circuits for logic operations such as AND and OR (where a 1 is interpreted as true and a 0 as false, so that, for instance, 1 AND 0 = 0; see Boolean algebra). The ALU has several to more than a hundred registers that temporarily hold results of its computations for further arithmetic operations or for transfer to main memory.
The circuits in the CPU control section provide branch instructions, which make elementary decisions about what instruction to execute next. For example, a branch instruction might be “If the result of the last ALU operation is negative, jump to location A in the program; otherwise, continue with the following instruction.” Such instructions allow “if-then-else” decisions in a program and execution of a sequence of instructions, such as a “while-loop” that repeatedly does some set of instructions while some condition is met. A related instruction is the subroutine call, which transfers execution to a subprogram and then, after the subprogram finishes, returns to the main program where it left off.
In a stored-program computer, programs and data in memory are indistinguishable. Both are bit patterns—strings of 0s and 1s—that may be interpreted either as data or as program instructions, and both are fetched from memory by the CPU. The CPU has a program counter that holds the memory address (location) of the next instruction to be executed. The basic operation of the CPU is the “fetch-decode-execute” cycle:
- Fetch the instruction from the address held in the program counter, and store it in a register.
- Decode the instruction. Parts of it specify the operation to be done, and parts specify the data on which it is to operate. These may be in CPU registers or in memory locations. If it is a branch instruction, part of it will contain the memory address of the next instruction to execute once the branch condition is satisfied.
- Fetch the operands, if any.
- Execute the operation if it is an ALU operation.
- Store the result (in a register or in memory), if there is one.
- Update the program counter to hold the next instruction location, which is either the next memory location or the address specified by a branch instruction.
At the end of these steps the cycle is ready to repeat, and it continues until a special halt instruction stops execution.
Steps of this cycle and all internal CPU operations are regulated by a clock that oscillates at a high frequency (now typically measured in gigahertz, or billions of cycles per second). Another factor that affects performance is the “word” size—the number of bits that are fetched at once from memory and on which CPU instructions operate. Digital words now consist of 32 or 64 bits, though sizes from 8 to 128 bits are seen.
Processing instructions one at a time, or serially, often creates a bottleneck because many program instructions may be ready and waiting for execution. Since the early 1980s, CPU design has followed a style originally called reduced-instruction-set computing (RISC). This design minimizes the transfer of data between memory and CPU (all ALU operations are done only on data in CPU registers) and calls for simple instructions that can execute very quickly. As the number of transistors on a chip has grown, the RISC design requires a relatively small portion of the CPU chip to be devoted to the basic instruction set. The remainder of the chip can then be used to speed CPU operations by providing circuits that let several instructions execute simultaneously, or in parallel.
There are two major kinds of instruction-level parallelism (ILP) in the CPU, both first used in early supercomputers. One is the pipeline, which allows the fetch-decode-execute cycle to have several instructions under way at once. While one instruction is being executed, another can obtain its operands, a third can be decoded, and a fourth can be fetched from memory. If each of these operations requires the same time, a new instruction can enter the pipeline at each phase and (for example) five instructions can be completed in the time that it would take to complete one without a pipeline. The other sort of ILP is to have multiple execution units in the CPU—duplicate arithmetic circuits, in particular, as well as specialized circuits for graphics instructions or for floating-point calculations (arithmetic operations involving noninteger numbers, such as 3.27). With this “superscalar” design, several instructions can execute at once.
Both forms of ILP face complications. A branch instruction might render preloaded instructions in the pipeline useless if they entered it before the branch jumped to a new part of the program. Also, superscalar execution must determine whether an arithmetic operation depends on the result of another operation, since they cannot be executed simultaneously. CPUs now have additional circuits to predict whether a branch will be taken and to analyze instructional dependencies. These have become highly sophisticated and can frequently rearrange instructions to execute more of them in parallel.
Main memory
The earliest forms of computer main memory were mercury delay lines, which were tubes of mercury that stored data as ultrasonic waves, and cathode-ray tubes, which stored data as charges on the tubes’ screens. The magnetic drum, invented about 1948, used an iron oxide coating on a rotating drum to store data and programs as magnetic patterns.
In a binary computer any bistable device (something that can be placed in either of two states) can represent the two possible bit values of 0 and 1 and can thus serve as computer memory. Magnetic-core memory, the first relatively cheap RAM device, appeared in 1952. It was composed of tiny, doughnut-shaped ferrite magnets threaded on the intersection points of a two-dimensional wire grid. These wires carried currents to change the direction of each core’s magnetization, while a third wire threaded through the doughnut detected its magnetic orientation.
The first integrated circuit (IC) memory chip appeared in 1971. IC memory stores a bit in a transistor-capacitor combination. The capacitor holds a charge to represent a 1 and no charge for a 0; the transistor switches it between these two states. Because a capacitor charge gradually decays, IC memory is dynamic RAM (DRAM), which must have its stored values refreshed periodically (every 20 milliseconds or so). There is also static RAM (SRAM), which does not have to be refreshed. Although faster than DRAM, SRAM uses more transistors and is thus more costly; it is used primarily for CPU internal registers and cache memory.
In addition to main memory, computers generally have special video memory (VRAM) to hold graphical images, called bitmaps, for the computer display. This memory is often dual-ported—a new image can be stored in it at the same time that its current data is being read and displayed.
It takes time to specify an address in a memory chip, and, since memory is slower than a CPU, there is an advantage to memory that can transfer a series of words rapidly once the first address is specified. One such design is known as synchronous DRAM (SDRAM), which became widely used by 2001.
Nonetheless, data transfer through the “bus”—the set of wires that connect the CPU to memory and peripheral devices—is a bottleneck. For that reason, CPU chips now contain cache memory—a small amount of fast SRAM. The cache holds copies of data from blocks of main memory. A well-designed cache allows up to 85–90 percent of memory references to be done from it in typical programs, giving a several-fold speedup in data access.
The time between two memory reads or writes (cycle time) was about 17 microseconds (millionths of a second) for early core memory and about 1 microsecond for core in the early 1970s. The first DRAM had a cycle time of about half a microsecond, or 500 nanoseconds (billionths of a second), and today it is 20 nanoseconds or less. An equally important measure is the cost per bit of memory. The first DRAM stored 128 bytes (1 byte = 8 bits) and cost about $10, or $80,000 per megabyte (millions of bytes). In 2001 DRAM could be purchased for less than $0.25 per megabyte. This vast decline in cost made possible graphical user interfaces (GUIs), the display fonts that word processors use, and the manipulation and visualization of large masses of data by scientific computers.
Secondary memory
Secondary memory on a computer is storage for data and programs not in use at the moment. In addition to punched cards and paper tape, early computers also used magnetic tape for secondary storage. Tape is cheap, either on large reels or in small cassettes, but has the disadvantage that it must be read or written sequentially from one end to the other.
IBM introduced the first magnetic disk, the RAMAC, in 1955; it held 5 megabytes and rented for $3,200 per month. Magnetic disks are platters coated with iron oxide, like tape and drums. An arm with a tiny wire coil, the read/write (R/W) head, moves radially over the disk, which is divided into concentric tracks composed of small arcs, or sectors, of data. Magnetized regions of the disk generate small currents in the coil as it passes, thereby allowing it to “read” a sector; similarly, a small current in the coil will induce a local magnetic change in the disk, thereby “writing” to a sector. The disk rotates rapidly (up to 15,000 rotations per minute), and so the R/W head can rapidly reach any sector on the disk.
Early disks had large removable platters. In the 1970s IBM introduced sealed disks with fixed platters known as Winchester disks—perhaps because the first ones had two 30-megabyte platters, suggesting the Winchester 30-30 rifle. Not only was the sealed disk protected against dirt, the R/W head could also “fly” on a thin air film, very close to the platter. By putting the head closer to the platter, the region of oxide film that represented a single bit could be much smaller, thus increasing storage capacity. This basic technology is still used.
Refinements have included putting multiple platters—10 or more—in a single disk drive, with a pair of R/W heads for the two surfaces of each platter in order to increase storage and data transfer rates. Even greater gains have resulted from improving control of the radial motion of the disk arm from track to track, resulting in denser distribution of data on the disk. By 2002 such densities had reached over 8,000 tracks per centimetre (20,000 tracks per inch), and a platter the diameter of a coin could hold over a gigabyte of data. In 2002 an 80-gigabyte disk cost about $200—only one ten-millionth of the 1955 cost and representing an annual decline of nearly 30 percent, similar to the decline in the price of main memory.
Optical storage devices—CD-ROM (compact disc, read-only memory) and DVD-ROM (digital videodisc, or versatile disc)—appeared in the mid-1980s and ’90s. They both represent bits as tiny pits in plastic, organized in a long spiral like a phonograph record, written and read with lasers. A CD-ROM can hold 2 gigabytes of data, but the inclusion of error-correcting codes (to correct for dust, small defects, and scratches) reduces the usable data to 650 megabytes. DVDs are denser, have smaller pits, and can hold 17 gigabytes with error correction.
Optical storage devices are slower than magnetic disks, but they are well suited for making master copies of software or for multimedia (audio and video) files that are read sequentially. There are also writable and rewritable CD-ROMs (CD-R and CD-RW) and DVD-ROMs (DVD-R and DVD-RW) that can be used like magnetic tapes for inexpensive archiving and sharing of data.
The decreasing cost of memory continues to make new uses possible. A single CD-ROM can store 100 million words, more than twice as many words as are contained in the printed Encyclopædia Britannica. A DVD can hold a feature-length motion picture. Nevertheless, even larger and faster storage systems, such as three-dimensional optical media, are being developed for handling data for computer simulations of nuclear reactions, astronomical data, and medical data, including X-ray images. Such applications typically require many terabytes (1 terabyte = 1,000 gigabytes) of storage, which can lead to further complications in indexing and retrieval.
David HemmendingerPeripherals
Computer peripherals are devices used to input information and instructions into a computer for storage or processing and to output the processed data. In addition, devices that enable the transmission and reception of data between computers are often classified as peripherals.
Input devices
A plethora of devices falls into the category of input peripheral. Typical examples include keyboards, mice, trackballs, pointing sticks, joysticks, digital tablets, touch pads, and scanners.
Keyboards contain mechanical or electromechanical switches that change the flow of current through the keyboard when depressed. A microprocessor embedded in the keyboard interprets these changes and sends a signal to the computer. In addition to letter and number keys, most keyboards also include “function” and “control” keys that modify input or send special commands to the computer.
Mechanical mice and trackballs operate alike, using a rubber or rubber-coated ball that turns two shafts connected to a pair of encoders that measure the horizontal and vertical components of a user’s movement, which are then translated into cursor movement on a computer monitor. Optical mice employ a light beam and camera lens to translate motion of the mouse into cursor movement.
Pointing sticks, which are popular on many laptop systems, employ a technique that uses a pressure-sensitive resistor. As a user applies pressure to the stick, the resistor increases the flow of electricity, thereby signaling that movement has taken place. Most joysticks operate in a similar manner.
Digital tablets and touch pads are similar in purpose and functionality. In both cases, input is taken from a flat pad that contains electrical sensors that detect the presence of either a special tablet pen or a user’s finger, respectively.
A scanner is somewhat akin to a photocopier. A light source illuminates the object to be scanned, and the varying amounts of reflected light are captured and measured by an analog-to-digital converter attached to light-sensitive diodes. The diodes generate a pattern of binary digits that are stored in the computer as a graphical image.
Output devices
Printers are a common example of output devices. New multifunction peripherals that integrate printing, scanning, and copying into a single device are also popular. Computer monitors are sometimes treated as peripherals. High-fidelity sound systems are another example of output devices often classified as computer peripherals. Manufacturers have announced devices that provide tactile feedback to the user—“force feedback” joysticks, for example. This highlights the complexity of classifying peripherals—a joystick with force feedback is truly both an input and an output peripheral.
Early printers often used a process known as impact printing, in which a small number of pins were driven into a desired pattern by an electromagnetic printhead. As each pin was driven forward, it struck an inked ribbon and transferred a single dot the size of the pinhead to the paper. Multiple dots combined into a matrix to form characters and graphics, hence the name dot matrix. Another early print technology, daisy-wheel printers, made impressions of whole characters with a single blow of an electromagnetic printhead, similar to an electric typewriter. Laser printers have replaced such printers in most commercial settings. Laser printers employ a focused beam of light to etch patterns of positively charged particles on the surface of a cylindrical drum made of negatively charged organic, photosensitive material. As the drum rotates, negatively charged toner particles adhere to the patterns etched by the laser and are transferred to the paper. Another, less expensive printing technology developed for the home and small businesses is inkjet printing. The majority of inkjet printers operate by ejecting extremely tiny droplets of ink to form characters in a matrix of dots—much like dot matrix printers.
Computer display devices have been in use almost as long as computers themselves. Early computer displays employed the same cathode-ray tubes (CRTs) used in television and radar systems. The fundamental principle behind CRT displays is the emission of a controlled stream of electrons that strike light-emitting phosphors coating the inside of the screen. The screen itself is divided into multiple scan lines, each of which contains a number of pixels—the rough equivalent of dots in a dot matrix printer. The resolution of a monitor is determined by its pixel size. More recent liquid crystal displays (LCDs) rely on liquid crystal cells that realign incoming polarized light. The realigned beams pass through a filter that permits only those beams with a particular alignment to pass. By controlling the liquid crystal cells with electrical charges, various colours or shades are made to appear on the screen.
Communication devices
The most familiar example of a communication device is the common telephone modem (from modulator/demodulator). Modems modulate, or transform, a computer’s digital message into an analog signal for transmission over standard telephone networks, and they demodulate the analog signal back into a digital message on reception. In practice, telephone network components limit analog data transmission to about 48 kilobits per second. Standard cable modems operate in a similar manner over cable television networks, which have a total transmission capacity of 30 to 40 megabits per second over each local neighbourhood “loop.” (Like Ethernet cards, cable modems are actually local area network devices, rather than true modems, and transmission performance deteriorates as more users share the loop.) Asymmetric digital subscriber line (ADSL) modems can be used for transmitting digital signals over a local dedicated telephone line, provided there is a telephone office nearby—in theory, within 5,500 metres (18,000 feet) but in practice about a third of that distance. ADSL is asymmetric because transmission rates differ to and from the subscriber: 8 megabits per second “downstream” to the subscriber and 1.5 megabits per second “upstream” from the subscriber to the service provider. In addition to devices for transmitting over telephone and cable wires, wireless communication devices exist for transmitting infrared, radiowave, and microwave signals.
Peripheral interfaces
A variety of techniques have been employed in the design of interfaces to link computers and peripherals. An interface of this nature is often termed a bus. This nomenclature derives from the presence of many paths of electrical communication (e.g., wires) bundled or joined together in a single device. Multiple peripherals can be attached to a single bus—the peripherals need not be homogeneous. An example is the small computer systems interface (SCSI; pronounced “scuzzy”). This popular standard allows heterogeneous devices to communicate with a computer by sharing a single bus. Under the auspices of various national and international organizations, many such standards have been established by manufacturers and users of computers and peripherals.
Buses can be loosely classified as serial or parallel. Parallel buses have a relatively large number of wires bundled together that enable data to be transferred in parallel. This increases the throughput, or rate of data transfer, between the peripheral and computer. SCSI buses are parallel buses. Examples of serial buses include the universal serial bus (USB). USB has an interesting feature in that the bus carries not only data to and from the peripheral but also electrical power. Examples of other peripheral integration schemes include integrated drive electronics (IDE) and enhanced integrated drive electronics (EIDE). Predating USB, these two schemes were designed initially to support greater flexibility in adapting hard disk drives to a variety of different computer makers.
William Morton PottengerThe Editors of Encyclopaedia BritannicaMicroprocessor integrated circuits
Before integrated circuits (ICs) were invented, computers used circuits of individual transistors and other electrical components—resistors, capacitors, and diodes—soldered to a circuit board. In 1959 Jack Kilby at Texas Instruments Incorporated, and Robert Noyce at Fairchild Semiconductor Corporation filed patents for integrated circuits. Kilby found how to make all the circuit components out of germanium, the semiconductor material then commonly used for transistors. Noyce used silicon, which is now almost universal, and found a way to build the interconnecting wires as well as the components on a single silicon chip, thus eliminating all soldered connections except for those joining the IC to other components. Brief discussions of IC circuit design, fabrication, and some design issues follow. For a more extensive discussion, see semiconductor and integrated circuit.
Design
Today IC design starts with a circuit description written in a hardware-specification language (like a programming language) or specified graphically with a digital design program. Computer simulation programs then test the design before it is approved. Another program translates the basic circuit layout into a multilayer network of electronic elements and wires.
Fabrication
The IC itself is formed on a silicon wafer cut from a cylinder of pure silicon—now commonly 200–300 mm (8–12 inches) in diameter. Since more chips can be cut from a larger wafer, the material unit cost of a chip goes down with increasing wafer size. A photographic image of each layer of the circuit design is made, and photolithography is used to expose a corresponding circuit of “resist” that has been put on the wafer. The unwanted resist is washed off and the exposed material then etched. This process is repeated to form various layers, with silicon dioxide (glass) used as electrical insulation between layers.
Between these production stages, the silicon is doped with carefully controlled amounts of impurities such as arsenic and boron. These create an excess and a deficiency, respectively, of electrons, thus creating regions with extra available negative charges (n-type) and positive “holes” (p-type). These adjacent doped regions form p-n junction transistors, with electrons (in the n-type regions) and holes (in the p-type regions) migrating through the silicon conducting electricity.
Layers of metal or conducting polycrystalline silicon are also placed on the chip to provide interconnections between its transistors. When the fabrication is complete, a final layer of insulating glass is added, and the wafer is sawed into individual chips. Each chip is tested, and those that pass are mounted in a protective package with external contacts.
Transistor size
The size of transistor elements continually decreases in order to pack more on a chip. In 2001 a transistor commonly had dimensions of 0.25 micron (or micrometre; 1 micron = 10−6 metre), and 0.1 micron was projected for 2006. This latter size would allow 200 million transistors to be placed on a chip (rather than about 40 million in 2001). Because the wavelength of visible light is too great for adequate resolution at such a small scale, ultraviolet photolithography techniques are being developed. As sizes decrease further, electron beam or X-ray techniques will become necessary. Each such advance requires new fabrication plants, costing several billion dollars apiece.
Power consumption
The increasing speed and density of elements on chips have led to problems of power consumption and dissipation. Central processing units now typically dissipate about 50 watts of power—as much heat per square inch as an electric stove element generates—and require “heat sinks” and cooling fans or even water cooling systems. As CPU speeds increase, cryogenic cooling systems may become necessary. Because storage battery technologies have not kept pace with power consumption in portable devices, there has been renewed interest in gallium arsenide (GaAs) chips. GaAs chips can run at higher speeds and consume less power than silicon chips. (GaAs chips are also more resistant to radiation, a factor in military and space applications.) Although GaAs chips have been used in supercomputers for their speed, the brittleness of GaAs has made it too costly for most ordinary applications. One promising idea is to bond a GaAs layer to a silicon substrate for easier handling. Nevertheless, GaAs is not yet in common use except in some high-frequency communication systems.
Future CPU designs
Since the early 1990s, researchers have discussed two speculative but intriguing new approaches to computation—quantum computing and molecular (DNA) computing. Each offers the prospect of highly parallel computation and a way around the approaching physical constraints to Moore’s law.
Quantum computing
According to quantum mechanics, an electron has a binary (two-valued) property known as “spin.” This suggests another way of representing a bit of information. While single-particle information storage is attractive, it would be difficult to manipulate. The fundamental idea of quantum computing, however, depends on another feature of quantum mechanics: that atomic-scale particles are in a “superposition” of all their possible states until an observation, or measurement, “collapses” their various possible states into one actual state. This means that if a system of particles—known as quantum bits, or qubits—can be “entangled” together, all the possible combinations of their states can be simultaneously used to perform a computation, at least in theory.
Indeed, while a few algorithms have been devised for quantum computing, building useful quantum computers has been more difficult. This is because the qubits must maintain their coherence (quantum entanglement) with one another while preventing decoherence (interaction with the external environment). As of 2000, the largest entangled system built contained only seven qubits.
Molecular computing
In 1994 Leonard Adleman, a mathematician at the University of Southern California, demonstrated the first DNA computer by solving a simple example of what is known as the traveling salesman problem. A traveling salesman problem—or, more generally, certain types of network problems in graph theory—asks for a route (or the shortest route) that begins at a certain city, or “node,” and travels to each of the other nodes exactly once. Digital computers, and sufficiently persistent humans, can solve for small networks by simply listing all the possible routes and comparing them, but as the number of nodes increases, the number of possible routes grows exponentially and soon (beyond about 50 nodes) overwhelms the fastest supercomputer. While digital computers are generally constrained to performing calculations serially, Adleman realized that he could take advantage of DNA molecules to perform a “massively parallel” calculation. He began by selecting different nucleotide sequences to represent each city and every direct route between two cities. He then made trillions of copies of each of these nucleotide strands and mixed them in a test tube. In less than a second he had the answer, albeit along with some hundred trillion spurious answers. Using basic recombinant DNA laboratory techniques, Adleman then took one week to isolate the answer—culling first molecules that did not start and end with the proper cities (nucleotide sequences), then those that did not contain the proper number of cities, and finally those that did not contain each city exactly once.
Although Adleman’s network contained only seven nodes—an extremely trivial problem for digital computers—it was the first demonstration of the feasibility of DNA computing. Since then Erik Winfree, a computer scientist at the California Institute of Technology, has demonstrated that nonbiologic DNA variants (such as branched DNA) can be adapted to store and process information. DNA and quantum computing remain intriguing possibilities that, even if they prove impractical, may lead to further advances in the hardware of future computers.
Operating systems
Role of operating systems
Operating systems manage a computer’s resources—memory, peripheral devices, and even CPU access—and provide a battery of services to the user’s programs. UNIX, first developed for minicomputers and now widely used on both PCs and mainframes, is one example; Linux (a version of UNIX), Microsoft Corporation’s Windows XP, and Apple Computer’s OS X are others.
One may think of an operating system as a set of concentric shells. At the centre is the bare processor, surrounded by layers of operating system routines to manage input/output (I/O), memory access, multiple processes, and communication among processes. User programs are located in the outermost layers. Each layer insulates its inner layer from direct access, while providing services to its outer layer. This architecture frees outer layers from having to know all the details of lower-level operations, while protecting inner layers and their essential services from interference.
Early computers had no operating system. A user loaded a program from paper tape by employing switches to specify its memory address, to start loading, and to run the program. When the program finished, the computer halted. The programmer had to have knowledge of every computer detail, such as how much memory it had and the characteristics of I/O devices used by the program.
It was quickly realized that this was an inefficient use of resources, particularly as the CPU was largely idle while waiting for relatively slow I/O devices to finish tasks such as reading and writing data. If instead several programs could be loaded at once and coordinated to interleave their steps of computation and I/O, more work could be done. The earliest operating systems were small supervisor programs that did just that: they coordinated several programs, accepting commands from the operator, and provided them all with basic I/O operations. These were known as multiprogrammed systems.
A multiprogrammed system must schedule its programs according to some priority rule, such as “shortest jobs first.” It must protect them from mutual interference to prevent an addressing error in a program from corrupting the data or code of another. It must ensure noninterference during I/O so that output from several programs does not get commingled or input misdirected. It might also have to record the CPU time of each job for billing purposes.
Modern types of operating systems
Multiuser systems
An extension of multiprogramming systems was developed in the 1960s, known variously as multiuser or time-sharing systems. (For a history of this development, see the section Time-sharing from Project MAC to UNIX.) Time-sharing allows many people to interact with a computer at once, each getting a small portion of the CPU’s time. If the CPU is fast enough, it will appear to be dedicated to each user, particularly as a computer can perform many functions while waiting for each user to finish typing the latest commands.
Multiuser operating systems employ a technique known as multiprocessing, or multitasking (as do most single-user systems today), in which even a single program may consist of many separate computational activities, called processes. The system must keep track of active and queued processes, when each process must access secondary memory to retrieve and store its code and data, and the allocation of other resources, such as peripheral devices.
Since main memory was very limited, early operating systems had to be as small as possible to leave room for other programs. To overcome some of this limitation, operating systems use virtual memory, one of many computing techniques developed during the late 1950s under the direction of Tom Kilburn at the University of Manchester, England. Virtual memory gives each process a large address space (memory that it may use), often much larger than the actual main memory. This address space resides in secondary memory (such as tape or disks), from which portions are copied into main memory as needed, updated as necessary, and returned when a process is no longer active. Even with virtual memory, however, some “kernel” of the operating system has to remain in main memory. Early UNIX kernels occupied tens of kilobytes; today they occupy more than a megabyte, and PC operating systems are comparable, largely because of the declining cost of main memory.
Operating systems have to maintain virtual memory tables to keep track of where each process’s address space resides, and modern CPUs provide special registers to make this more efficient. Indeed, much of an operating system consists of tables: tables of processes, of files and their locations (directories), of resources used by each process, and so on. There are also tables of user accounts and passwords that help control access to the user’s files and protect them against accidental or malicious interference.
Thin systems
While minimizing the memory requirements of operating systems for standard computers has been important, it has been absolutely essential for small, inexpensive, specialized devices such as personal digital assistants (PDAs), “smart” cellular telephones, portable devices for listening to compressed music files, and Internet kiosks. Such devices must be highly reliable, fast, and secure against break-ins or corruption—a cellular telephone that “freezes” in the middle of calls would not be tolerated. One might argue that these traits should characterize any operating system, but PC users seem to have become quite tolerant of frequent operating system failures that require restarts.
Reactive systems
Still more limited are embedded, or real-time, systems. These are small systems that run the control processors embedded in machinery from factory production lines to home appliances. They interact with their environment, taking in data from sensors and making appropriate responses. Embedded systems are known as “hard” real-time systems if they must guarantee schedules that handle all events even in a worst case and “soft” if missed deadlines are not fatal. An aircraft control system is a hard real-time system, as a single flight error might be fatal. An airline reservation system, on the other hand, is a soft real-time system, since a missed booking is rarely catastrophic.
Many of the features of modern CPUs and operating systems are inappropriate for hard real-time systems. For example, pipelines and superscalar multiple execution units give high performance at the expense of occasional delays when a branch prediction fails and a pipeline is filled with unneeded instructions. Likewise, virtual memory and caches give good memory-access times on the average, but sometimes they are slow. Such variability is inimical to meeting demanding real-time schedules, and so embedded processors and their operating systems must generally be relatively simple.
Operating system design approaches
Operating systems may be proprietary or open. Mainframe systems have largely been proprietary, supplied by the computer manufacturer. In the PC domain, Microsoft offers its proprietary Windows systems, Apple has supplied Mac OS for its line of Macintosh computers, and there are few other choices. The best-known open system has been UNIX, originally developed by Bell Laboratories and supplied freely to universities. In its Linux variant it is available for a wide range of PCs, workstations, and, most recently, IBM mainframes.
Open-source software is copyrighted, but its author grants free use, often including the right to modify it provided that use of the new version is not restricted. Linux is protected by the Free Software Foundation’s “GNU General Public License,” like all the other software in the extensive GNU project, and this protection permits users to modify Linux and even to sell copies, provided that this right of free use is preserved in the copies.
One consequence of the right of free use is that numerous authors have contributed to the GNU-Linux work, adding many valuable components to the basic system. Although quality control is managed voluntarily and some have predicted that Linux would not survive heavy commercial use, it has been remarkably successful and seems well on its way to becoming the version of UNIX on mainframes and on PCs used as Internet servers.
There are other variants of the UNIX system; some are proprietary, though most are now freely used, at least noncommercially. They all provide some type of graphical user interface. Although Mac OS has been proprietary, its current version, Mac OS X, is built on UNIX.
Proprietary systems such as Microsoft’s Windows 98, 2000, and XP provide highly integrated systems. All operating systems provide file directory services, for example, but a Microsoft system might use the same window display for a directory as for a World Wide Web browser. Such an integrated approach makes it more difficult for nonproprietary software to use Windows capabilities, a feature that has been an issue in antitrust lawsuits against Microsoft.
Networking
Computer communication may occur through wires, optical fibres, or radio transmissions. Wired networks may use shielded coaxial cable, similar to the wire connecting a television to a videocassette recorder or an antenna. They can also use simpler unshielded wiring with modular connectors similar to telephone wires. Optical fibres can carry more signals than wires; they are often used for linking buildings on a college campus or corporate site and increasingly for longer distances as telephone companies update their networks. Microwave radio also carries computer network signals, generally as part of long-distance telephone systems. Low-power microwave radio is becoming common for wireless networks within a building.
Local area networks
Local area networks (LANs) connect computers within a building or small group of buildings. A LAN may be configured as (1) a bus, a main channel to which nodes or secondary channels are connected in a branching structure, (2) a ring, in which each computer is connected to two neighbouring computers to form a closed circuit, or (3) a star, in which each computer is linked directly to a central computer and only indirectly to one another. Each of these has advantages, though the bus configuration has become the most common.
Even if only two computers are connected, they must follow rules, or protocols, to communicate. For example, one might signal “ready to send” and wait for the other to signal “ready to receive.” When many computers share a network, the protocol might include a rule “talk only when it is your turn” or “do not talk when anyone else is talking.” Protocols must also be designed to handle network errors.
The most common LAN design since the mid-1970s has been the bus-connected Ethernet, originally developed at Xerox PARC. Every computer or other device on an Ethernet has a unique 48-bit address. Any computer that wants to transmit listens for a carrier signal that indicates that a transmission is under way. If it detects none, it starts transmitting, sending the address of the recipient at the start of its transmission. Every system on the network receives each message but ignores those not addressed to it. While a system is transmitting, it also listens, and if it detects a simultaneous transmission, it stops, waits for a random time, and retries. The random time delay before retrying reduces the probability that they will collide again. This scheme is known as carrier sense multiple access with collision detection (CSMA/CD). It works very well until a network is moderately heavily loaded, and then it degrades as collisions become more frequent.
The first Ethernet had a capacity of about 2 megabits per second, and today 10- and 100-megabit-per-second Ethernet is common, with gigabit-per-second Ethernet also in use. Ethernet transceivers (transmitter-receivers) for PCs are inexpensive and easily installed.
A recent standard for wireless Ethernet, known as Wi-Fi, is becoming common for small office and home networks. Using frequencies from 2.4 to 5 gigahertz (GHz), such networks can transfer data at rates up to 600 megabits per second. Early in 2002 another Ethernet-like standard was released. Known as HomePlug, the first version could transmit data at about 8 megabits per second through a building’s existing electrical power infrastructure. A later version could achieve rates of 1 gigabit per second.
Wide area networks
Wide area networks (WANs) span cities, countries, and the globe, generally using telephone lines and satellite links. The Internet connects multiple WANs; as its name suggests, it is a network of networks. Its success stems from early support by the U.S. Department of Defense, which developed its precursor, ARPANET, to let researchers communicate readily and share computer resources. Its success is also due to its flexible communication technique. The emergence of the Internet in the 1990s as not only a communication medium but also one of the principal focuses of computer use may be the most significant development in computing in the past several decades. For more on the history and technical details of Internet communication protocols, see Internet.
Computer software
Software denotes programs that run on computers. John Tukey, a statistician at Princeton University and Bell Laboratories, is generally credited with introducing the term in 1958 (as well as coining the word bit for binary digit). Initially software referred primarily to what is now called system software—an operating system and the utility programs that come with it, such as those to compile (translate) programs into machine code and load them for execution. This software came with a computer when it was bought or leased. In 1969 IBM decided to “unbundle” its software and sell it separately, and software soon became a major income source for manufacturers as well as for dedicated software firms.
Business and personal software
Business software generally must handle large amounts of data but relatively little computation, although that has changed somewhat in recent years. Office software typically includes word processors, spreadsheets, database programs, and tools for designing public presentations.
A spreadsheet is a type of accounting program. Unlike specialized accounting programs (e.g., payroll and office records), an important function of spreadsheets is their ability to explore “What if?” scenarios. A spreadsheet not only holds tables of data but also defines relationships among their rows and columns. For example, if the profit on a product is defined in terms of various costs—materials, manufacturing, and shipping—it is easy to ask “What if we use cheaper materials that require more manufacturing expense?”
A database is an organized collection of data, or records. Databases organize information to answer questions such as “What companies in the Southwest bought more than 100 of our products last year?” or “Which products made by Acme Manufacturing are in low supply?” Such software is often integrated so that a database report or spreadsheet table can be added to a document composed with a word processor, frequently with illustrative graphs. Today even the most trivial data can effortlessly be glorified by presenting it in a polychromatic bar chart with three-dimensional shading.
Scientific and engineering software
Scientific software is typically used to solve differential equations. (Differential equations are used to describe continuous actions or processes that depend on some other factors.) Although some differential equations have relatively simple mathematical solutions, exact solutions of many differential equations are very difficult to obtain. Computers, however, can be used to obtain useful approximate solutions, particularly when a problem is split into simpler spatial or temporal parts. Nevertheless, large-scale problems often require parallel computation on supercomputers or clusters of small computers that share the work.
There are numerous standard libraries of equation-solving software—some commercial, some distributed by national organizations in several countries. Another kind of software package does symbolic mathematics, obtaining exact solutions by algebraic manipulations. Two of the most widely used symbolic packages are Mathematica and Maple.
Scientific visualization software couples high-performance graphics with the output of equation solvers to yield vivid displays of models of physical systems. As with spreadsheets, visualization software lets an experimenter vary initial conditions or parameters. Observing the effect of such changes can help in improving models, as well as in understanding the original system.
Visualization is an essential feature of computer-aided engineering (CAE) and computer-aided design (CAD). An engineer can design a bridge, use modeling software to display it, and study it under different loads. CAE software can translate drawings into the precise specification of the parts of a mechanical system. Computer chips themselves are designed with CAD programs that let an engineer write a specification for part of a chip, simulate its behaviour in detail, test it thoroughly, and then generate the layouts for the photolithographic process that puts the circuit on the silicon.
Astronomical sky surveys, weather forecasting, and medical imaging—such as magnetic resonance imaging, CAT scans, and DNA analyses—create very large collections of data. Scientific computation today uses the same kinds of powerful statistical and pattern-analysis techniques as many business applications.
Internet and collaborative software
Among the most commonly used personal Internet software are “browsers” for displaying information located on the World Wide Web, newsreaders for reading “newsgroups” located on USENET, file-sharing programs for downloading files, and communication software for e-mail, as well as “instant messaging” and “chat room” programs that allow people to carry on conversations in real time. All of these applications are used for both personal and business activities.
Other common Internet software includes Web search engines and “Web-crawling” programs that traverse the Web to gather and classify information. Web-crawling programs are a kind of agent software, a term for programs that carry out routine tasks for a user. They stem from artificial intelligence research and carry out some of the tasks of librarians, but they are at a severe disadvantage. Although Web pages may have “content-tag” index terms, not all do, nor are there yet accepted standards for their use. Web search engines must use heuristic methods to determine the quality of Web page information as well as its content. Many details are proprietary, but they may use techniques such as finding “hubs” and “authorities” (pages with many links to and from other Web sites). Such strategies can be very effective, though the need for a Web version of card catalogs has not vanished.
A different kind of Internet use depends on the vast number of computers connected to the Internet that are idle much of the time. Rather than run a “screen-saver” program, these computers can run software that lets them collaborate in the analysis of some difficult problem. Two examples are the [email protected] project, which distributes portions of radio telescope data for analysis that might help in the search for extraterrestrial intelligence (SETI), and the “Great Internet Mersenne Prime Search” (GIMPS), which parcels out tasks to test for large prime numbers.
The Internet has also become a business tool, and the ability to collect and store immense amounts of information has given rise to data warehousing and data mining. The former is a term for unstructured collections of data and the latter a term for its analysis. Data mining uses statistics and other mathematical tools to find patterns of information. For more information concerning business on the Internet, see e-commerce.
Games and entertainment
Computer games are nearly as old as digital computers and have steadily developed in sophistication. Chinook, a recent checkers (draughts) program, is widely believed to be better than any human player, and the IBM Deep Blue chess program beat world champion Garry Kasparov in 1996. These programs have demonstrated the power of modern computers, as well as the strength of good heuristics for strategy. On the other hand, such brute-force search heuristics have failed to produce a go-playing program that can defeat even moderately skilled players because there are too many possible moves in this Japanese game for simple quantification.
After board games, the earliest computer games were text-based adventures—in which players explored virtual worlds, sought treasure, and fought enemies by reading and typing simple commands. Such games resembled military simulation programs first used in the early 1950s. Contemporary games, however, depend on high-performance computer graphics. Played on arcade machines, special game computers for home use, or PCs, they use the same capabilities as simulation and visualization programs. A related area is computer-generated (CG) animation for films and video.
David HemmendingerHistory of computing
A computer might be described with deceptive simplicity as “an apparatus that performs routine calculations automatically.” Such a definition would owe its deceptiveness to a naive and narrow view of calculation as a strictly mathematical process. In fact, calculation underlies many activities that are not normally thought of as mathematical. Walking across a room, for instance, requires many complex, albeit subconscious, calculations. Computers, too, have proved capable of solving a vast array of problems, from balancing a checkbook to even—in the form of guidance systems for robots—walking across a room.
Before the true power of computing could be realized, therefore, the naive view of calculation had to be overcome. The inventors who laboured to bring the computer into the world had to learn that the thing they were inventing was not just a number cruncher, not merely a calculator. For example, they had to learn that it was not necessary to invent a new computer for every new calculation and that a computer could be designed to solve numerous problems, even problems not yet imagined when the computer was built. They also had to learn how to tell such a general problem-solving computer what problem to solve. In other words, they had to invent programming.
They had to solve all the heady problems of developing such a device, of implementing the design, of actually building the thing. The history of the solving of these problems is the history of the computer. That history is covered in this section, and links are provided to entries on many of the individuals and companies mentioned. In addition, see the articles computer science and supercomputer.
Early history
Computer precursors
The abacus
The earliest known calculating device is probably the abacus. It dates back at least to 1100 BCE and is still in use today, particularly in Asia. Now, as then, it typically consists of a rectangular frame with thin parallel rods strung with beads. Long before any systematic positional notation was adopted for the writing of numbers, the abacus assigned different units, or weights, to each rod. This scheme allowed a wide range of numbers to be represented by just a few beads and, together with the invention of zero in India, may have inspired the invention of the Hindu-Arabic number system. In any case, abacus beads can be readily manipulated to perform the common arithmetical operations—addition, subtraction, multiplication, and division—that are useful for commercial transactions and in bookkeeping.
The abacus is a digital device; that is, it represents values discretely. A bead is either in one predefined position or another, representing unambiguously, say, one or zero.
Analog calculators: from Napier’s logarithms to the slide rule
Calculating devices took a different turn when John Napier, a Scottish mathematician, published his discovery of logarithms in 1614. As any person can attest, adding two 10-digit numbers is much simpler than multiplying them together, and the transformation of a multiplication problem into an addition problem is exactly what logarithms enable. This simplification is possible because of the following logarithmic property: the logarithm of the product of two numbers is equal to the sum of the logarithms of the numbers. By 1624, tables with 14 significant digits were available for the logarithms of numbers from 1 to 20,000, and scientists quickly adopted the new labour-saving tool for tedious astronomical calculations.
Most significant for the development of computing, the transformation of multiplication into addition greatly simplified the possibility of mechanization. Analog calculating devices based on Napier’s logarithms—representing digital values with analogous physical lengths—soon appeared. In 1620 Edmund Gunter, the English mathematician who coined the terms cosine and cotangent, built a device for performing navigational calculations: the Gunter scale, or, as navigators simply called it, the gunter. About 1632 an English clergyman and mathematician named William Oughtred built the first slide rule, drawing on Napier’s ideas. That first slide rule was circular, but Oughtred also built the first rectangular one in 1633. The analog devices of Gunter and Oughtred had various advantages and disadvantages compared with digital devices such as the abacus. What is important is that the consequences of these design decisions were being tested in the real world.
Digital calculators: from the Calculating Clock to the Arithmometer
In 1623 the German astronomer and mathematician Wilhelm Schickard built the first calculator. He described it in a letter to his friend the astronomer Johannes Kepler, and in 1624 he wrote again to explain that a machine he had commissioned to be built for Kepler was, apparently along with the prototype, destroyed in a fire. He called it a Calculating Clock, which modern engineers have been able to reproduce from details in his letters. Even general knowledge of the clock had been temporarily lost when Schickard and his entire family perished during the Thirty Years’ War.
But Schickard may not have been the true inventor of the calculator. A century earlier, Leonardo da Vinci sketched plans for a calculator that were sufficiently complete and correct for modern engineers to build a calculator on their basis.
The first calculator or adding machine to be produced in any quantity and actually used was the Pascaline, or Arithmetic Machine, designed and built by the French mathematician-philosopher Blaise Pascal between 1642 and 1644. It could only do addition and subtraction, with numbers being entered by manipulating its dials. Pascal invented the machine for his father, a tax collector, so it was the first business machine too (if one does not count the abacus). He built 50 of them over the next 10 years.
In 1671 the German mathematician-philosopher Gottfried Wilhelm von Leibniz designed a calculating machine called the Step Reckoner. (It was first built in 1673.) The Step Reckoner expanded on Pascal’s ideas and did multiplication by repeated addition and shifting.
Leibniz was a strong advocate of the binary number system. Binary numbers are ideal for machines because they require only two digits, which can easily be represented by the on and off states of a switch. When computers became electronic, the binary system was particularly appropriate because an electrical circuit is either on or off. This meant that on could represent true, off could represent false, and the flow of current would directly represent the flow of logic.
Leibniz was prescient in seeing the appropriateness of the binary system in calculating machines, but his machine did not use it. Instead, the Step Reckoner represented numbers in decimal form, as positions on 10-position dials. Even decimal representation was not a given: in 1668 Samuel Morland invented an adding machine specialized for British money—a decidedly nondecimal system.
Pascal’s, Leibniz’s, and Morland’s devices were curiosities, but with the Industrial Revolution of the 18th century came a widespread need to perform repetitive operations efficiently. With other activities being mechanized, why not calculation? In 1820 Charles Xavier Thomas de Colmar of France effectively met this challenge when he built his Arithmometer, the first commercial mass-produced calculating device. It could perform addition, subtraction, multiplication, and, with some more elaborate user involvement, division. Based on Leibniz’s technology, it was extremely popular and sold for 90 years. In contrast to the modern calculator’s credit-card size, the Arithmometer was large enough to cover a desktop.
The Jacquard loom
Calculators such as the Arithmometer remained a fascination after 1820, and their potential for commercial use was well understood. Many other mechanical devices built during the 19th century also performed repetitive functions more or less automatically, but few had any application to computing. There was one major exception: the Jacquard loom, invented in 1804–05 by a French weaver, Joseph-Marie Jacquard.
The Jacquard loom was a marvel of the Industrial Revolution. A textile-weaving loom, it could also be called the first practical information-processing device. The loom worked by tugging various-coloured threads into patterns by means of an array of rods. By inserting a card punched with holes, an operator could control the motion of the rods and thereby alter the pattern of the weave. Moreover, the loom was equipped with a card-reading device that slipped a new card from a prepunched deck into place every time the shuttle was thrown, so that complex weaving patterns could be automated.
What was extraordinary about the device was that it transferred the design process from a labour-intensive weaving stage to a card-punching stage. Once the cards had been punched and assembled, the design was complete, and the loom implemented the design automatically. The Jacquard loom, therefore, could be said to be programmed for different patterns by these decks of punched cards.
For those intent on mechanizing calculations, the Jacquard loom provided important lessons: the sequence of operations that a machine performs could be controlled to make the machine do something quite different; a punched card could be used as a medium for directing the machine; and, most important, a device could be directed to perform different tasks by feeding it instructions in a sort of language—i.e., making the machine programmable.
It is not too great a stretch to say that, in the Jacquard loom, programming was invented before the computer. The close relationship between the device and the program became apparent some 20 years later, with Charles Babbage’s invention of the first computer.
The first computer
By the second decade of the 19th century, a number of ideas necessary for the invention of the computer were in the air. First, the potential benefits to science and industry of being able to automate routine calculations were appreciated, as they had not been a century earlier. Specific methods to make automated calculation more practical, such as doing multiplication by adding logarithms or by repeating addition, had been invented, and experience with both analog and digital devices had shown some of the benefits of each approach. The Jacquard loom (as described in the previous section, Computer precursors) had shown the benefits of directing a multipurpose device through coded instructions, and it had demonstrated how punched cards could be used to modify those instructions quickly and flexibly. It was a mathematical genius in England who began to put all these pieces together.
The Difference Engine
Charles Babbage was an English mathematician and inventor: he invented the cowcatcher, reformed the British postal system, and was a pioneer in the fields of operations research and actuarial science. It was Babbage who first suggested that the weather of years past could be read from tree rings. He also had a lifelong fascination with keys, ciphers, and mechanical dolls.
As a founding member of the Royal Astronomical Society, Babbage had seen a clear need to design and build a mechanical device that could automate long, tedious astronomical calculations. He began by writing a letter in 1822 to Sir Humphry Davy, president of the Royal Society, about the possibility of automating the construction of mathematical tables—specifically, logarithm tables for use in navigation. He then wrote a paper, “On the Theoretical Principles of the Machinery for Calculating Tables,” which he read to the society later that year. (It won the Royal Society’s first Gold Medal in 1823.) Tables then in use often contained errors, which could be a life-and-death matter for sailors at sea, and Babbage argued that, by automating the production of the tables, he could assure their accuracy. Having gained support in the society for his Difference Engine, as he called it, Babbage next turned to the British government to fund development, obtaining one of the world’s first government grants for research and technological development.
Babbage approached the project very seriously: he hired a master machinist, set up a fireproof workshop, and built a dustproof environment for testing the device. Up until then calculations were rarely carried out to more than 6 digits; Babbage planned to produce 20- or 30-digit results routinely. The Difference Engine was a digital device: it operated on discrete digits rather than smooth quantities, and the digits were decimal (0–9), represented by positions on toothed wheels, rather than the binary digits that Leibniz favoured (but did not use). When one of the toothed wheels turned from 9 to 0, it caused the next wheel to advance one position, carrying the digit just as Leibniz’s Step Reckoner calculator had operated.
The Difference Engine was more than a simple calculator, however. It mechanized not just a single calculation but a whole series of calculations on a number of variables to solve a complex problem. It went far beyond calculators in other ways as well. Like modern computers, the Difference Engine had storage—that is, a place where data could be held temporarily for later processing—and it was designed to stamp its output into soft metal, which could later be used to produce a printing plate.
Nevertheless, the Difference Engine performed only one operation. The operator would set up all of its data registers with the original data, and then the single operation would be repeatedly applied to all of the registers, ultimately producing a solution. Still, in complexity and audacity of design, it dwarfed any calculating device then in existence.
The full engine, designed to be room-size, was never built, at least not by Babbage. Although he sporadically received several government grants—governments changed, funding often ran out, and he had to personally bear some of the financial costs—he was working at or near the tolerances of the construction methods of the day, and he ran into numerous construction difficulties. All design and construction ceased in 1833, when Joseph Clement, the machinist responsible for actually building the machine, refused to continue unless he was prepaid. (The completed portion of the Difference Engine is on permanent exhibition at the Science Museum in London.)
The Analytical Engine
While working on the Difference Engine, Babbage began to imagine ways to improve it. Chiefly he thought about generalizing its operation so that it could perform other kinds of calculations. By the time the funding had run out in 1833, he had conceived of something far more revolutionary: a general-purpose computing machine called the Analytical Engine.
The Analytical Engine was to be a general-purpose, fully program-controlled, automatic mechanical digital computer. It would be able to perform any calculation set before it. Before Babbage there is no evidence that anyone had ever conceived of such a device, let alone attempted to build one. The machine was designed to consist of four components: the mill, the store, the reader, and the printer. These components are the essential components of every computer today. The mill was the calculating unit, analogous to the central processing unit (CPU) in a modern computer; the store was where data were held prior to processing, exactly analogous to memory and storage in today’s computers; and the reader and printer were the input and output devices.
As with the Difference Engine, the project was far more complex than anything theretofore built. The store was to be large enough to hold 1,000 50-digit numbers; this was larger than the storage capacity of any computer built before 1960. The machine was to be steam-driven and run by one attendant. The printing capability was also ambitious, as it had been for the Difference Engine: Babbage wanted to automate the process as much as possible, right up to producing printed tables of numbers.
The reader was another new feature of the Analytical Engine. Data (numbers) were to be entered on punched cards, using the card-reading technology of the Jacquard loom. Instructions were also to be entered on cards, another idea taken directly from Jacquard. The use of instruction cards would make it a programmable device and far more flexible than any machine then in existence. Another element of programmability was to be its ability to execute instructions in other than sequential order. It was to have a kind of decision-making ability in its conditional control transfer, also known as conditional branching, whereby it would be able to jump to a different instruction depending on the value of some data. This extremely powerful feature was missing in many of the early computers of the 20th century.
By most definitions, the Analytical Engine was a real computer as understood today—or would have been, had not Babbage run into implementation problems again. Actually building his ambitious design was judged infeasible given the current technology, and Babbage’s failure to generate the promised mathematical tables with his Difference Engine had dampened enthusiasm for further government funding. Indeed, it was apparent to the British government that Babbage was more interested in innovation than in constructing tables.
All the same, Babbage’s Analytical Engine was something new under the sun. Its most revolutionary feature was the ability to change its operation by changing the instructions on punched cards. Until this breakthrough, all the mechanical aids to calculation were merely calculators or, like the Difference Engine, glorified calculators. The Analytical Engine, although not actually completed, was the first machine that deserved to be called a computer.
Lady Lovelace, the first programmer
The distinction between calculator and computer, although clear to Babbage, was not apparent to most people in the early 19th century, even to the intellectually adventuresome visitors at Babbage’s soirees—with the exception of a young girl of unusual parentage and education.
Augusta Ada King, the countess of Lovelace, was the daughter of the poet Lord Byron and the mathematically inclined Anne Millbanke. One of her tutors was Augustus De Morgan, a famous mathematician and logician. Because Byron was involved in a notorious scandal at the time of her birth, Ada’s mother encouraged her mathematical and scientific interests, hoping to suppress any inclination to wildness she may have inherited from her father.
Toward that end, Lady Lovelace attended Babbage’s soirees and became fascinated with his Difference Engine. She also corresponded with him, asking pointed questions. It was his plan for the Analytical Engine that truly fired her imagination, however. In 1843, at age 27, she had come to understand it well enough to publish the definitive paper explaining the device and drawing the crucial distinction between this new thing and existing calculators. The Analytical Engine, she argued, went beyond the bounds of arithmetic. Because it operated on general symbols rather than on numbers, it established “a link…between the operations of matter and the abstract mental processes of the most abstract branch of mathematical science.” It was a physical device that was capable of operating in the realm of abstract thought.
Lady Lovelace rightly reported that this was not only something no one had built, it was something that no one before had even conceived. She went on to become the world’s only expert on the process of sequencing instructions on the punched cards that the Analytical Engine used; that is, she became the world’s first computer programmer.
One feature of the Analytical Engine was its ability to place numbers and instructions temporarily in its store and return them to its mill for processing at an appropriate time. This was accomplished by the proper sequencing of instructions and data in its reader, and the ability to reorder instructions and data gave the machine a flexibility and power that was hard to grasp. The first electronic digital computers of a century later lacked this ability. It was remarkable that a young scholar realized its importance in 1840, and it would be 100 years before anyone would understand it so well again. In the intervening century, attention would be diverted to the calculator and other business machines.
Early business machines
Throughout the 19th century, business machines were coming into common use. Calculators became available as a tool of commerce in 1820 (see the earlier section Digital calculators), and in 1874 the Remington Arms Company, Inc., sold the first commercially viable typewriter. Other machines were invented for other specific business tasks. None of these machines was a computer, but they did advance the state of practical mechanical knowledge—knowledge that would be used in computers later.
One of these machines was invented in response to a sort of constitutional crisis in the United States: the census tabulator.
Herman Hollerith’s census tabulator
The U.S. Constitution mandates that a census of the population be performed every 10 years. The first attempt at any mechanization of the census was in 1870, when statistical data were transcribed onto a rolling paper tape displayed through a small slotted window. As the size of America’s population exploded in the 19th century and the number of census questions expanded, the urgency of further mechanization became increasingly clear.
After graduating from the Columbia University School of Mines, New York City, in 1879, Herman Hollerith obtained his first job with one of his former professors, William P. Trowbridge, who had received a commission as a special agent for the 1880 census. It was while employed at the Census Office that Hollerith first saw the pressing need for automating the tabulation of statistical data.
Over the next 10 years Hollerith refined his ideas, obtaining his first patent in 1884 for a machine to punch and count cards. He then organized the health records for Baltimore, Maryland, for New York City, and for the state of New Jersey—all in preparation for winning the contract to tabulate the 1890 U.S. Census. The success of the U.S. census opened European governments to Hollerith’s machines. Most notably, a contract with the Russian government, signed on December 15, 1896, may have induced him to incorporate as the Tabulating Machine Company on December 5, 1896.
Other early business machine companies
Improvements in calculators continued: by the 1880s they could add in the accumulation of partial results, store past results, and print. Then, in 1892, William Seward Burroughs, who along with two other St. Louis, Missouri, businessmen had started the American Arithmometer Company in 1886 in order to build adding machines, obtained a patent for one of the first truly practical and commercially successful calculators. Burroughs died in 1898, and his company was reorganized as the Burroughs Adding Machine Company in Detroit, Michigan, in 1905.
All the calculators—and virtually all the information-processing devices—sold at this time were designed for commercial purposes, not scientific research. By the turn of the century, commercial calculating devices were in common use, as were other special-purpose machines such as one that generated serial numbers for banknotes. As a result, many of the business machine companies in the United States were doing well, including Hollerith’s Tabulating Machine Company.
In 1911 several of these companies combined to form the Computing-Tabulating-Recording Company, or CTR. In 1914 Thomas J. Watson, Sr., left his sales manager position at the National Cash Register Company to become president of CTR, and 10 years later CTR changed its name to International Business Machines Corporation, or IBM. In the second half of the century, IBM would become the giant of the world computer industry, but such commercial gains did not take place until enormous progress had been made in the theoretical understanding of the modern computer during the remarkable decades of the 1930s and ’40s. (This progress is described in the next section, Invention of the modern computer.)
Invention of the modern computer
Early experiments
As the technology for realizing a computer was being honed by the business machine companies in the early 20th century, the theoretical foundations were being laid in academia. During the 1930s two important strains of computer-related research were being pursued in the United States at two universities in Cambridge, Massachusetts. One strain produced the Differential Analyzer, the other a series of devices ending with the Harvard Mark IV.
Vannevar Bush’s Differential Analyzer
In 1930 an engineer named Vannevar Bush at the Massachusetts Institute of Technology (MIT) developed the first modern analog computer. The Differential Analyzer, as he called it, was an analog calculator that could be used to solve certain classes of differential equations, a type of problem common in physics and engineering applications that is often very tedious to solve. Variables were represented by shaft motion, and addition and multiplication were accomplished by feeding the values into a set of gears. Integration was carried out by means of a knife-edged wheel rotating at a variable radius on a circular table. The individual mechanical integrators were then interconnected to solve a set of differential equations.
The Differential Analyzer proved highly useful, and a number of them were built and used at various universities. Still the device was limited to solving this one class of problem, and, as is the case for all analog devices, it produced approximate, albeit practical, solutions. Nevertheless, important applications for analog computers and analog-digital hybrid computers still exist, particularly for simulating complicated dynamical systems such as aircraft flight, nuclear power plant operations, and chemical reactions.
Howard Aiken’s digital calculators
While Bush was working on analog computing at MIT, across town Harvard professor Howard Aiken was working with digital devices for calculation. He had begun to realize in hardware something like Babbage’s Analytical Engine, which he had read about. Starting in 1937, he laid out detailed plans for a series of four calculating machines of increasing sophistication, based on different technologies, from the largely mechanical Mark I to the electronic Mark IV.
Aiken was methodically exploring the technological advances made since the mechanical assembly and steam power available to Babbage. Electromagnetic relay circuits were already being used in business machines, and the vacuum tube—a switch with no moving parts, very high speed action, and greater reliability than electromechanical relays—was quickly put to use in the early experimental machines.
The business machines of the time used plugboards (something like telephone switchboards) to route data manually, and Aiken chose not to use them for the specification of instructions. This turned out to make his machine much easier to program than the more famous ENIAC, designed somewhat later, which had to be manually rewired for each program.
From 1939 to 1944 Aiken, in collaboration with IBM, developed his first fully functional computer, known as the Harvard Mark I. The machine, like Babbage’s, was huge: more than 50 feet (15 metres) long, weighing five tons, and consisting of about 750,000 separate parts, it was mostly mechanical. For input and output it used three paper-tape readers, two card readers, a card punch, and two typewriters. It took between three and six seconds to add two numbers. Aiken developed three more such machines (Mark II–IV) over the next few years and is credited with developing the first fully automatic large-scale calculator.
The Turing machine
Alan Turing, while a mathematics student at the University of Cambridge, was inspired by German mathematician David Hilbert’s formalist program, which sought to demonstrate that any mathematical problem can potentially be solved by an algorithm—that is, by a purely mechanical process. Turing interpreted this to mean a computing machine and set out to design one capable of resolving all mathematical problems, but in the process he proved in his seminal paper “On Computable Numbers, with an Application to the Entscheidungsproblem [‘Halting Problem’]” (1936) that no such universal mathematical solver could ever exist.
In order to design his machine (known to posterity as the “Turing machine”), he needed to find an unambiguous definition of the essence of a computer. In doing so, Turing worked out in great detail the basic concepts of a universal computing machine—that is, a computing machine that could, at least in theory, do anything that a special-purpose computing device could do. In particular, it would not be limited to doing arithmetic. The internal states of the machine could represent numbers, but they could equally well represent logic values or letters. In fact, Turing believed that everything could be represented symbolically, even abstract mental states, and he was one of the first advocates of the artificial-intelligence position that computers can potentially “think.”
Turing’s work up to this point was entirely abstract, entirely a theoretical demonstration. Nevertheless, he made it clear from the start that his results implied the possibility of building a machine of the sort he described. His work characterized the abstract essence of any computing device so well that it was in effect a challenge to actually build one.
Turing’s work had an immediate effect on only a small number of academics at a few universities who were interested in the concept of computing machinery. It had no immediate effect on the growing industry of business machines, all of which were special-purpose devices. But to the few who were interested, Turing’s work was an inspiration to pursue something of which most of the world had not even conceived: a universal computing machine.
Pioneering work
The Atanasoff-Berry Computer
It was generally believed that the first electronic digital computers were the Colossus, built in England in 1943, and the ENIAC, built in the United States in 1945. However, the first special-purpose electronic computer may actually have been invented by John Vincent Atanasoff, a physicist and mathematician at Iowa State College (now Iowa State University), during 1937–42. (Atanasoff also claimed to have invented the term analog computer to describe machines such as Vannevar Bush’s Differential Analyzer.) Together with his graduate assistant Clifford E. Berry, Atanasoff built a successful small prototype in 1939 for the purpose of testing two ideas central to his design: capacitors to store data in binary form and electronic logic circuits to perform addition and subtraction. They then began the design and construction of a larger, more general-purpose computer, known as the Atanasoff-Berry Computer, or ABC.
Various components of the ABC were designed and built from 1939 to 1942, but development was discontinued with the onset of World War II. The ABC featured about 300 vacuum tubes for control and arithmetic calculations, use of binary numbers, logic operations (instead of direct counting), memory capacitors, and punched cards as input/output units. (At Atanasoff’s invitation, another early computer pioneer, John Mauchly, stayed at his home and was freely shown his work for several days in June 1941. For more on the ramifications of this visit, see BTW: Computer patent wars.)
The first computer network
Between 1940 and 1946 George Stibitz and his team at Bell Laboratories built a series of machines with telephone technologies—i.e., employing electromechanical relays. These were the first machines to serve more than one user and the first to work remotely over telephone lines. However, because they were based on slow mechanical relays rather than electronic switches, they became obsolete almost as soon as they were constructed.
Konrad Zuse
Meanwhile, in Germany, engineer Konrad Zuse had been thinking about calculating machines. He was advised by a calculator manufacturer in 1937 that the field was a dead end and that every computing problem had already been solved. Zuse had something else in mind, though.
For one thing, Zuse worked in binary from the beginning. All of his prototype machines, built in 1936, used binary representation in order to simplify construction. This had the added advantage of making the connection with logic clearer, and Zuse worked out the details of how the operations of logic (e.g., AND, OR, and NOT) could be mapped onto the design of the computer’s circuits. (English mathematician George Boole had shown the connection between logic and mathematics in the mid-19th century, developing an algebra of logic now known as Boolean algebra.) Zuse also spent more time than his predecessors and contemporaries developing software for his computer, the language in which it was to be programmed. (His contributions to programming are examined in the section Programming languages.) Although all his early prewar machines were really calculators—not computers—his Z3, completed in December 1941 (and destroyed on April 6, 1945, during an Allied air raid on Berlin), was the first program-controlled processor.
Because all Zuse’s work was done in relative isolation, he knew little about work on computers in the United States and England, and, when the war began, the isolation became complete.
The following section, Developments during World War II, examines the development during the 1940s of the first fully functional digital computers.
Developments during World War II
Colossus
The exigencies of war gave impetus and funding to computer research. For example, in Britain the impetus was code breaking. The Ultra project was funded with much secrecy to develop the technology necessary to crack ciphers and codes produced by the German electromechanical devices known as the Enigma and the Geheimschreiber (“Secret Writer”). The first in a series of important code-breaking machines, Colossus, also known as the Mark I, was built under the direction of Sir Thomas Flowers and delivered in December 1943 to the code-breaking operation at Bletchley Park, a government research centre north of London. It employed approximately 1,800 vacuum tubes for computations. Successively larger and more elaborate versions were built over the next two years.
The Ultra project had a gifted mathematician associated with the Bletchley Park effort, and one familiar with codes. Alan Turing, who had earlier articulated the concept of a universal computing device (described in the section The Turing machine), may have pushed the project farther in the direction of a general-purpose device than his government originally had in mind. Turing’s advocacy helped keep up government support for the project.
Although it lacked some characteristics now associated with computers, Colossus can plausibly be described as the first electronic digital computer, and it was certainly a key stepping stone to the development of the modern computer. Although Colossus was designed to perform specific cryptographic-related calculations, it could be used for more-generalized purposes. Its design pioneered the massive use of electronics in computation, and it embodied an insight from Flowers of the importance of storing data electronically within the machine. The operation at Bletchley foreshadowed the modern data centre.
Colossus was successful in its intended purpose: the German messages it helped to decode provided information about German battle orders, supplies, and personnel; it also confirmed that an Allied deception campaign, Operation Fortitude, was working.
The series of Colossus computers were disassembled after the war, and most information about them remained classified until the 1990s. In 1996 the basic Colossus machine was rebuilt and switched on at Bletchley Park.
The Z4
In Germany, Konrad Zuse began construction of the Z4 in 1943 with funding from the Air Ministry. Like his Z3 (described in the section Konrad Zuse), the Z4 used electromechanical relays, in part because of the difficulty in acquiring the roughly 2,000 necessary vacuum tubes in wartime Germany. The Z4 was evacuated from Berlin in early 1945, and it eventually wound up in Hinterstein, a small village in the Bavarian Alps, where it remained until Zuse brought it to the Federal Technical Institute in Zürich, Switzerland, for refurbishing in 1950. Although unable to continue with hardware development, Zuse made a number of advances in software design.
Zuse’s use of floating-point representation for numbers—the significant digits, known as the mantissa, are stored separately from a pointer to the decimal point, known as the exponent, allowing a very large range of numbers to be handled—was far ahead of its time. In addition, Zuse developed a rich set of instructions, handled infinite values correctly, and included a “no-op”—that is, an instruction that did nothing. Only significant experience in programming would show the need for something so apparently useless.
The Z4’s program was punched on used movie film and was separate from the mechanical memory for data (in other words, there was no stored program). The machine was relatively reliable (it normally ran all night unattended), but it had no decision-making ability. Addition took 0.5 to 1.25 seconds, multiplication 3.5 seconds.
ENIAC
In the United States, government funding went to a project led by John Mauchly, J. Presper Eckert, Jr., and their colleagues at the Moore School of Electrical Engineering at the University of Pennsylvania; their objective was an all-electronic computer. Under contract to the army and under the direction of Herman Goldstine, work began in early 1943 on the Electronic Numerical Integrator and Computer (ENIAC). The next year, mathematician John von Neumann, already on full-time leave from the Institute for Advanced Studies (IAS), Princeton, New Jersey, for various government research projects (including the Manhattan Project), began frequent consultations with the group.
ENIAC was something less than the dream of a universal computer. Designed for the specific purpose of computing values for artillery range tables, it lacked some features that would have made it a more generally useful machine. Like Colossus but unlike Howard Aiken’s machine (described in the section Early experiments), it used plugboards for communicating instructions to the machine; this had the advantage that, once the instructions were thus “programmed,” the machine ran at electronic speed. Instructions read from a card reader or other slow mechanical device would not have been able to keep up with the all-electronic ENIAC. The disadvantage was that it took days to rewire the machine for each new problem. This was such a liability that only with some generosity could it be called programmable.
Nevertheless, ENIAC was the most powerful calculating device built to date. Like Charles Babbage’s Analytical Engine and the Colossus, but unlike Aiken’s Mark I, Konrad Zuse’s Z4, and George Stibitz’s telephone-savvy machine, it did have conditional branching—that is, it had the ability to execute different instructions or to alter the order of execution of instructions based on the value of some data. (For instance, IF X > 5 THEN GO TO LINE 23.) This gave ENIAC a lot of flexibility and meant that, while it was built for a specific purpose, it could be used for a wider range of problems.
ENIAC was enormous. It occupied the 50-by-30-foot (15-by-9-metre) basement of the Moore School, where its 40 panels were arranged, U-shaped, along three walls. Each of the units was about 2 feet wide by 2 feet deep by 8 feet high (0.6 by 0.6 by 2.4 metres). With approximately 18,000 vacuum tubes, 70,000 resistors, 10,000 capacitors, 6,000 switches, and 1,500 relays, it was easily the most complex electronic system theretofore built. ENIAC ran continuously (in part to extend tube life), generating 150 kilowatts of heat, and could execute up to 5,000 additions per second, several orders of magnitude faster than its electromechanical predecessors. Colossus, ENAIC, and subsequent computers employing vacuum tubes are known as first-generation computers. (With 1,500 mechanical relays, ENIAC was still transitional to later, fully electronic computers.)
Completed by February 1946, ENIAC had cost the government $400,000, and the war it was designed to help win was over. Its first task was doing calculations for the construction of a hydrogen bomb. A portion of the machine is on exhibit at the Smithsonian Institution in Washington, D.C.
Toward the classical computer
Bigger brains
The computers built during the war were built under unusual constraints. The British work was largely focused on code breaking, the American work on computing projectile trajectories and calculations for the atomic bomb. The computers were built as special-purpose devices, although they often embodied more general-purpose computing capabilities than their specifications called for. The vacuum tubes in these machines were not entirely reliable, but with no moving parts they were more reliable than the electromechanical switches they replaced, and they were much faster. Reliability was an issue, since Colossus used some 1,500 tubes and ENIAC on the order of 18,000. But ENIAC was, by virtue of its electronic realization, 1,000 times faster than the Harvard Mark I. Such speed meant that the machine could perform calculations that were theretofore beyond human ability. Although tubes were a great advance over the electromechanical realization of Aiken or the steam-and-mechanical model of Babbage, the basic architecture of the machines (that is, the functions they were able to perform) was not much advanced beyond Babbage’s Difference Engine and Analytical Engine. In fact, the original name for ENIAC was Electronic Difference Analyzer, and it was built to perform much like Babbage’s Difference Engine.
After the war, efforts focused on fulfilling the idea of a general-purpose computing device. In 1945, before ENIAC was even finished, planning began at the Moore School for ENIAC’s successor, the Electronic Discrete Variable Automatic Computer, or EDVAC. (Planning for EDVAC also set the stage for an ensuing patent fight; see BTW: Computer patent wars.) ENIAC was hampered, as all previous electronic computers had been, by the need to use one vacuum tube to store each bit, or binary digit. The feasible number of vacuum tubes in a computer also posed a practical limit on storage capacity—beyond a certain point, vacuum tubes are bound to burn out as fast as they can be changed. For EDVAC, Eckert had a new idea for storage.
In 1880 French physicists Pierre and Jacques Curie had discovered that applying an electric current to a quartz crystal would produce a characteristic vibration and vice versa. During the 1930s at Bell Laboratories, William Shockley, later coinventor of the transistor, had demonstrated a device—a tube, called a delay line, containing water and ethylene glycol—for effecting a predictable delay in information transmission. Eckert had already built and experimented in 1943 with such a delay line (using mercury) in conjunction with radar research, and sometime in 1944 he hit upon the new idea of placing a quartz crystal at each end of the mercury delay line in order to sustain and modify the resulting pattern. In effect, he invented a new storage device. Whereas ENIAC required one tube per bit, EDVAC could use a delay line and 10 vacuum tubes to store 1,000 bits. Before the invention of the magnetic core memory and the transistor, which would eliminate the need for vacuum tubes altogether, the mercury delay line was instrumental in increasing computer storage and reliability.
Von Neumann’s “Preliminary Discussion”
But the design of the modern, or classical, computer did not fully crystallize until the publication of a 1946 paper by Arthur Burks, Herman Goldstine, and John von Neumann titled “Preliminary Discussion of the Logical Design of an Electronic Computing Instrument”. Although the paper was essentially a synthesis of ideas currently “in the air,” it is frequently cited as the birth certificate of computer science.
Among the principles enunciated in the paper were that data and instructions should be kept in a single store and that instructions should be encoded so as to be modifiable by other instructions. This was an extremely critical decision, because it meant that one program could be treated as data by another program. Zuse had considered and rejected this possibility as too dangerous. But its inclusion by von Neumann’s group made possible high-level programming languages and most of the advances in software of the following 50 years. Subsequently, computers with stored programs would be known as von Neumann machines.
One problem that the stored-program idea solved was the need for rapid access to instructions. Colossus and ENIAC had used plugboards, which had the advantage of enabling the instructions to be read in electronically, rather than by much slower mechanical card readers, but it also had the disadvantage of making these first-generation machines very hard to program. But if the instructions could be stored in the same electronic memory that held the data, they could be accessed as quickly as needed. One immediately obvious consequence was that EDVAC would need a lot more memory than ENIAC.
The first stored-program machines
Government secrecy hampered British efforts to build on wartime computer advances, but engineers in Britain still beat the Americans to the goal of building the first stored-program digital computer. At the University of Manchester, Frederic C. Williams and Tom Kilburn built a simple stored-program computer, known as the Baby, in 1948. This was built to test their invention of a way to store information on a cathode-ray tube that enabled direct access (in contrast to the mercury delay line’s sequential access) to stored information. Although faster than Eckert’s storage method, it proved somewhat unreliable. Nevertheless, it became the preferred storage method for most of the early computers worldwide that were not already committed to mercury delay lines.
By 1949 Williams and Kilburn had extended the Baby to a full-size computer, the Manchester Mark I. This had two major new features that were to become computer standards: a two-level store and instruction modification registers (which soon evolved into index registers). A magnetic drum was added to provide a random-access secondary storage device. Until machines were fitted with index registers, every instruction that referred to an address that varied as the program ran—e.g., an array element—had to be preceded by instructions to alter its address to the current required value. Four months after the Baby first worked, the British government contracted the electronics firm of Ferranti to build a production computer based on the prospective Mark I. This became the Ferranti Mark I—the first commercial computer—of which nine were sold.
Kilburn, Williams, and colleagues at Manchester also came up with a breakthrough that would revolutionize how a computer executed instructions: they made it possible for the address portion of an instruction to be modified while the program was running. Before this, an instruction specified that a particular action—say, addition—was to be performed on data in one or more particular locations. Their innovation allowed the location to be modified as part of the operation of executing the instruction. This made it very easy to address elements within an array sequentially.
At the University of Cambridge, meanwhile, Maurice Wilkes and others built what is recognized as the first full-size, fully electronic, stored-program computer to provide a formal computing service for users. The Electronic Delay Storage Automatic Calculator (EDSAC) was built on the set of principles synthesized by von Neumann and, like the Manchester Mark I, became operational in 1949. Wilkes built the machine chiefly to study programming issues, which he realized would become as important as the hardware details.
Whirlwind
New hardware continued to be invented, though. In the United States, Jay Forrester of the Massachusetts Institute of Technology (MIT) and Jan Aleksander Rajchman of the Radio Corporation of America came up with a new kind of memory based on magnetic cores that was fast enough to enable MIT to build the first real-time computer, Whirlwind. A real-time computer is one that can respond seemingly instantly to basic instructions, thus allowing an operator to interact with a “running” computer.
