A central processing unit (CPU), also referred to as a central processor unit, The form, design, and implementation of CPUs have changed over the course of their history, but their fundamental operation remains much the same.
On large machines, CPUs require one or more integrated circuits in packages typically less than four centimeters square, with hundreds of connecting pins.
Two typical components of a CPU are the memory and decodes and executes them, calling on the ALU when necessary.
Not all computational systems rely on a central processing unit. An array processor or distributed computing model, problems are solved by a distributed interconnected set of processors.
Computers such as the stored-program computer.
The idea of a stored-program computer was already present in the design of J. Presper Eckert and John William Mauchly‘s ENIAC, but was initially omitted so that it could be finished sooner. On June 30, 1945, before ENIAC was made, mathematician John von Neumann distributed the paper entitled First Draft of a Report on the EDVAC. It was the outline of a stored-program computer that would eventually be completed in August 1949. EDVAC was designed to perform a certain number of instructions (or operations) of various types. These instructions could be combined to create useful programs for the EDVAC to run. Significantly, the programs written for EDVAC were stored in high-speed computer memory rather than specified by the physical wiring of the computer. This overcame a severe limitation of ENIAC, which was the considerable time and effort required to reconfigure the computer to perform a new task. With von Neumann’s design, the program, or software, that EDVAC ran could be changed simply by changing the contents of the memory.
Early CPUs were custom-designed as a part of a larger, sometimes one-of-a-kind, computer. However, this method of designing custom CPUs for a particular application has largely given way to the development of mass-produced processors that are made for many purposes. This standardization began in the era of discrete transistor mainframes and minicomputers and has rapidly accelerated with the popularization of the integrated circuit (IC). The IC has allowed increasingly complex CPUs to be designed and manufactured to tolerances on the order of nanometers. Both the miniaturization and standardization of CPUs have increased the presence of digital devices in modern life far beyond the limited application of dedicated computing machines. Modern microprocessors appear in everything from automobiles to cell phones and children’s toys.
While von Neumann is most often credited with the design of the stored-program computer because of his design of EDVAC, others before him, such as Konrad Zuse, had suggested and implemented similar ideas. The so-called Harvard architecture of the Harvard Mark I, which was completed before EDVAC, also utilized a stored-program design using punched paper tape rather than electronic memory. The key difference between the von Neumann and Harvard architectures is that the latter separates the storage and treatment of CPU instructions and data, while the former uses the same memory space for both. Most modern CPUs are primarily von Neumann in design, but elements of the Harvard architecture are commonly seen as well.
Relays and vacuum tubes (thermionic valves) were commonly used as switching elements; a useful computer requires thousands or tens of thousands of switching devices. The overall speed of a system is dependent on the speed of the switches. Tube computers like EDVAC tended to average eight hours between failures, whereas relay computers like the (slower, but earlier) Harvard Mark I failed very rarely. In the end, tube based CPUs became dominant because the significant speed advantages afforded generally outweighed the reliability problems. Most of these early synchronous CPUs ran at low clock rates compared to modern microelectronic designs (see below for a discussion of clock rate). Clock signal frequencies ranging from 100 kHz to 4 MHz were very common at this time, limited largely by the speed of the switching devices they were built with.
 Transistor and integrated circuit CPUs
The design complexity of CPUs increased as various technologies facilitated building smaller and more reliable electronic devices. The first such improvement came with the advent of the printed circuit boards containing discrete (individual) components.
During this period, a method of manufacturing many interconnected transistors in a compact space was developed. The Apollo guidance computer, usually contained up to a few score transistors. To build an entire CPU out of SSI ICs required thousands of individual chips, but still consumed much less space and power than earlier discrete transistor designs. As microelectronic technology advanced, an increasing number of transistors were placed on ICs, thus decreasing the quantity of individual ICs needed for a complete CPU. MSI and LSI (medium- and large-scale integration) ICs increased transistor counts to hundreds, and then thousands.
In 1964 
Transistor-based computers had several distinct advantages over their predecessors. Aside from facilitating increased reliability and lower power consumption, transistors also allowed CPUs to operate at much higher speeds because of the short switching time of a transistor in comparison to a tube or relay. Thanks to both the increased reliability as well as the dramatically increased speed of the switching elements (which were almost exclusively transistors by this time), CPU clock rates in the tens of megahertz were obtained during this period. Additionally while discrete transistor and IC CPUs were in heavy usage, new high-performance designs like Cray Inc.
In the 1970s the fundamental inventions by according to whom?] to microprocessors. Several CPUs can be combined in a single processing chip.
Previous generations of CPUs were implemented as discrete components and numerous small integrated circuits (ICs) on one or more circuit boards. Microprocessors, on the other hand, are CPUs manufactured on a very small number of ICs; usually just one. The overall smaller CPU size as a result of being implemented on a single die means faster switching time because of physical factors like decreased gate parasitic capacitance. This has allowed synchronous microprocessors to have clock rates ranging from tens of megahertz to several gigahertz. Additionally, as the ability to construct exceedingly small transistors on an IC has increased, the complexity and number of transistors in a single CPU has increased many fold. This widely observed trend is described by Moore’s law, which has proven to be a fairly accurate predictor of the growth of CPU (and other IC) complexity.
While the complexity, size, construction, and general form of CPUs have changed enormously since 1950, it is notable that the basic design and function has not changed much at all. Almost all common CPUs today can be very accurately[parallelism and other methods that extend the usefulness of the classical von Neumann model.
The fundamental operation of most CPUs, regardless of the physical form they take, is to execute a sequence of stored instructions called a program. The program is represented by a series of numbers that are kept in some kind of computer memory. There are four steps that nearly all CPUs use in their operation: fetch, decode, execute, and writeback.
The first step, fetch, involves retrieving an instruction (which is represented by a number or sequence of numbers) from program memory. The location in program memory is determined by a program counter (PC), which stores a number that identifies the current position in the program. After an instruction is fetched, the PC is incremented by the length of the instruction word in terms of memory units. Often, the instruction to be fetched must be retrieved from relatively slow memory, causing the CPU to stall while waiting for the instruction to be returned. This issue is largely addressed in modern processors by caches and pipeline architectures (see below).
The instruction that the CPU fetches from memory is used to determine what the CPU is to do. In the decode step, the instruction is broken up into parts that have significance to other portions of the CPU. The way in which the numerical instruction value is interpreted is defined by the CPU’s instruction set architecture (ISA). Often, one group of numbers in the instruction, called the opcode, indicates which operation to perform. The remaining parts of the number usually provide information required for that instruction, such as operands for an addition operation. Such operands may be given as a constant value (called an immediate value), or as a place to locate a value: a register or a memory address, as determined by some addressing mode. In older designs the portions of the CPU responsible for instruction decoding were unchangeable hardware devices. However, in more abstract and complicated CPUs and ISAs, a microprogram is often used to assist in translating instructions into various configuration signals for the CPU. This microprogram is sometimes rewritable so that it can be modified to change the way the CPU decodes instructions even after it has been manufactured.
After the fetch and decode steps, the execute step is performed. During this step, various portions of the CPU are connected so they can perform the desired operation. If, for instance, an addition operation was requested, the bitwise operations). If the addition operation produces a result too large for the CPU to handle, an arithmetic overflow flag in a flags register may also be set.
The final step, writeback, simply “writes back” the results of the execute step to some form of memory. Very often the results are written to some internal CPU register for quick access by subsequent instructions. In other cases results may be written to slower, but cheaper and larger,  Many instructions will also change the state of digits in a “flags” register. These flags can be used to influence how a program behaves, since they often indicate the outcome of various operations. For example, one type of “compare” instruction considers two values and sets a number in the flags register according to which one is greater. This flag could then be used by a later jump instruction to determine program flow.
After the execution of the instruction and writeback of the resulting data, the entire process repeats, with the next CPU cache, and therefore the access stage of the pipeline.
 Design and implementation
The basic concept of a CPU is as follows:
Hardwired into a CPU’s design is a list of basic operations it can perform, called an computer program is therefore a sequence of instructions, with each instruction including an opcode and that operation’s arguments.
The actual mathematical operation for each instruction is performed by a subunit of the CPU known as the arithmetic logic unit or ALU. In addition to using its ALU to perform operations, a CPU is also responsible for reading the next instruction from memory, reading data specified in arguments from memory, and writing results to memory.
In many CPU designs, an instruction set will clearly differentiate between operations that load data from memory, and those that perform math. In this case the data loaded from memory is stored in registers, and a mathematical operation takes no arguments but simply performs the math on the data in the registers and writes it to a new register, whose value a separate operation may then write to memory.
 Control unit
The control unit of the CPU contains circuitry that uses electrical signals to direct the entire computer system to carry out stored program instructions. The control unit does not execute program instructions; rather, it directs other parts of the system to do so. The control unit must communicate with both the arithmetic/logic unit and memory.
 Integer range
The way a CPU represents numbers is a design choice that affects the most basic ways in which the device functions. Some early digital computers used an electrical model of the common decimal (base ten) numeral system to represent numbers internally. A few other computers have used more exotic numeral systems like ternary (base three). Nearly all modern CPUs represent numbers in binary form, with each digit being represented by some two-valued physical quantity such as a “high” or “low” voltage.
Related to number representation is the size and precision of numbers that a CPU can represent. In the case of a binary CPU, a bit refers to one significant place in the numbers a CPU deals with. The number of bits (or numeral places) a CPU uses to represent numbers is often called “word size“, “bit width”, “data path width”, or “integer precision” when dealing with strictly integer numbers (as opposed to floating point). This number differs between architectures, and often within different parts of the very same CPU. For example, an 8-bit CPU deals with a range of numbers that can be represented by eight binary digits (each digit having two possible values), that is, 28 or 256 discrete numbers. In effect, integer size sets a hardware limit on the range of integers the software run by the CPU can utilize.
Integer range can also affect the number of locations in memory the CPU can address (locate). For example, if a binary CPU uses 32 bits to represent a memory address, and each memory address represents one octet (8 bits), the maximum quantity of memory that CPU can address is 232 octets, or 4 GiB. This is a very simple view of CPU address space, and many designs use more complex addressing methods like paging to locate more memory than their integer range would allow with a flat address space.
Higher levels of integer range require more structures to deal with the additional digits, and therefore more complexity, size, power usage, and general expense. It is not at all uncommon, therefore, to see 4- or 8-bit microcontrollers used in modern applications, even though CPUs with much higher range (such as 16, 32, 64, even 128-bit) are available. The simpler microcontrollers are usually cheaper, use less power, and therefore generate less heat, all of which can be major design considerations for electronic devices. However, in higher-end applications, the benefits afforded by the extra range (most often the additional address space) are more significant and often affect design choices. To gain some of the advantages afforded by both lower and higher bit lengths, many CPUs are designed with different bit widths for different portions of the device. For example, the IBM System/370 used a CPU that was primarily 32 bit, but it used 128-bit precision inside its floating point units to facilitate greater accuracy and range in floating point numbers. Many later CPU designs use similar mixed bit width, especially when the processor is meant for general-purpose usage where a reasonable balance of integer and floating point capability is required.
 Clock rate
The clock rate is the speed at which a microprocessor executes instructions. Every computer contains an internal clock that regulates the rate at which instructions are executed and synchronizes all the various computer components. The CPU requires a fixed number of clock ticks (or clock cycles) to execute each instruction. The faster the clock, the more instructions the CPU can execute per second.
Most CPUs, and indeed most sequential logic devices, are synchronous in nature. That is, they are designed and operate on assumptions about a synchronization signal. This signal, known as a clock signal, usually takes the form of a periodic square wave. By calculating the maximum time that electrical signals can move in various branches of a CPU’s many circuits, the designers can select an appropriate period for the clock signal.
This period must be longer than the amount of time it takes for a signal to move, or propagate, in the worst-case scenario. In setting the clock period to a value well above the worst-case propagation delay, it is possible to design the entire CPU and the way it moves data around the “edges” of the rising and falling clock signal. This has the advantage of simplifying the CPU significantly, both from a design perspective and a component-count perspective. However, it also carries the disadvantage that the entire CPU must wait on its slowest elements, even though some portions of it are much faster. This limitation has largely been compensated for by various methods of increasing CPU parallelism. (see below)
However, architectural improvements alone do not solve all of the drawbacks of globally synchronous CPUs. For example, a clock signal is subject to the delays of any other electrical signal. Higher clock rates in increasingly complex CPUs make it more difficult to keep the clock signal in phase (synchronized) throughout the entire unit. This has led many modern CPUs to require multiple identical clock signals to be provided to avoid delaying a single signal significantly enough to cause the CPU to malfunction. Another major issue as clock rates increase dramatically is the amount of heat that is dissipated by the CPU. The constantly changing clock causes many components to switch regardless of whether they are being used at that time. In general, a component that is switching uses more energy than an element in a static state. Therefore, as clock rate increases, so does heat dissipation, causing the CPU to require more effective cooling solutions.
One method of dealing with the switching of unneeded components is called 
The description of the basic operation of a CPU offered in the previous section describes the simplest form that a CPU can take. This type of CPU, usually referred to as subscalar, operates on and executes one instruction on one or two pieces of data at a time.
This process gives rise to an inherent inefficiency in subscalar CPUs. Since only one instruction is executed at a time, the entire CPU must wait for that instruction to complete before proceeding to the next instruction. As a result, the subscalar CPU gets “hung up” on instructions which take more than one clock cycle to complete execution. Even adding a second execution unit (see below) does not improve performance much; rather than one pathway being hung up, now two pathways are hung up and the number of unused transistors is increased. This design, wherein the CPU’s execution resources can operate on only one instruction at a time, can only possibly reach scalar performance (one instruction per clock). However, the performance is nearly always subscalar (less than one instruction per cycle).
Attempts to achieve scalar and better performance have resulted in a variety of design methodologies that cause the CPU to behave less linearly and more in parallel. When referring to parallelism in CPUs, two terms are generally used to classify these design techniques. Instruction level parallelism (ILP) seeks to increase the rate at which instructions are executed within a CPU (that is, to increase the utilization of on-die execution resources), and thread level parallelism (TLP) purposes to increase the number of threads (effectively individual programs) that a CPU can execute simultaneously. Each methodology differs both in the ways in which they are implemented, as well as the relative effectiveness they afford in increasing the CPU’s performance for an application.
 Instruction level parallelism
One of the simplest methods used to accomplish increased parallelism is to begin the first steps of instruction fetching and decoding before the prior instruction finishes executing. This is the simplest form of a technique known as instruction pipelining, and is utilized in almost all modern general-purpose CPUs. Pipelining allows more than one instruction to be executed at any given time by breaking down the execution pathway into discrete stages. This separation can be compared to an assembly line, in which an instruction is made more complete at each stage until it exits the execution pipeline and is retired.
Pipelining does, however, introduce the possibility for a situation where the result of the previous operation is needed to complete the next operation; a condition often termed data dependency conflict. To cope with this, additional care must be taken to check for these sorts of conditions and delay a portion of the instruction pipeline if this occurs. Naturally, accomplishing this requires additional circuitry, so pipelined processors are more complex than subscalar ones (though not very significantly so). A pipelined processor can become very nearly scalar, inhibited only by pipeline stalls (an instruction spending more than one clock cycle in a stage).
Further improvement upon the idea of instruction pipelining led to the development of a method that decreases the idle time of CPU components even further. Designs that are said to be superscalar include a long instruction pipeline and multiple identical execution units. In a superscalar pipeline, multiple instructions are read and passed to a dispatcher, which decides whether or not the instructions can be executed in parallel (simultaneously). If so they are dispatched to available execution units, resulting in the ability for several instructions to be executed simultaneously. In general, the more instructions a superscalar CPU is able to dispatch simultaneously to waiting execution units, the more instructions will be completed in a given cycle.
Most of the difficulty in the design of a superscalar CPU architecture lies in creating an effective dispatcher. The dispatcher needs to be able to quickly and correctly determine whether instructions can be executed in parallel, as well as dispatch them in such a way as to keep as many execution units busy as possible. This requires that the instruction pipeline is filled as often as possible and gives rise to the need in superscalar architectures for significant amounts of out-of-order execution crucial to maintaining high levels of performance. By attempting to predict which branch (or path) a conditional instruction will take, the CPU can minimize the number of times that the entire pipeline must wait until a conditional instruction is completed. Speculative execution often provides modest performance increases by executing portions of code that may not be needed after a conditional operation completes. Out-of-order execution somewhat rearranges the order in which instructions are executed to reduce delays due to data dependencies. Also in case of Single Instructions Multiple Data — a case when a lot of data from the same type has to be processed, modern processors can disable parts of the pipeline so that when a single instruction is executed many times, the CPU skips the fetch and decode phases and thus greatly increases performance on certain occasions, especially in highly monotonous program engines such as video creation software and photo processing.
In the case where a portion of the CPU is superscalar and part is not, the part which is not suffers a performance penalty due to scheduling stalls. The Intel P6, added superscalar capabilities to its floating point features, and therefore afforded a significant increase in floating point instruction performance.
Both simple pipelining and superscalar design increase a CPU’s ILP by allowing a single processor to complete execution of instructions at rates surpassing one instruction per cycle (IPC). Most modern CPU designs are at least somewhat superscalar, and nearly all general purpose CPUs designed in the last decade are superscalar. In later years some of the emphasis in designing high-ILP computers has been moved out of the CPU’s hardware and into its software interface, or ISA. The strategy of the very long instruction word (VLIW) causes some ILP to become implied directly by the software, reducing the amount of work the CPU must perform to boost ILP and thereby reducing the design’s complexity.
 Thread-level parallelism
Another strategy of achieving performance is to execute multiple programs or Flynn’s taxonomy, this strategy is known as Multiple Instructions-Multiple Data or MIMD.
One technology used for this purpose was multi-core microprocessor.
It was later recognized that finer-grain parallelism existed with a single program. A single program might have several threads (or functions) that could be executed separately or in parallel. Some of the earliest examples of this technology implemented simultaneous multithreading, where instructions of multiple threads are executed in parallel within one CPU clock cycle.
For several decades from the 1970s to early 2000s, the focus in designing high performance general purpose CPUs was largely on achieving high ILP through technologies such as pipelining, caches, superscalar execution, out-of-order execution, etc. This trend culminated in large, power-hungry CPUs such as the Intel Pentium 4. By the early 2000s, CPU designers were thwarted from achieving higher performance from ILP techniques due to the growing disparity between CPU operating frequencies and main memory operating frequencies as well as escalating CPU power dissipation owing to more esoteric ILP techniques.
CPU designers then borrowed ideas from commercial computing markets such as throughput computing, was more important than the performance of a single thread or program.
This reversal of emphasis is evidenced by the proliferation of dual and multiple core CMP (chip-level multiprocessing) designs and notably, Intel’s newer designs resembling its less superscalar Cell microprocessor.
 Data parallelism
A less common but increasingly important paradigm of CPUs (and indeed, computing in general) deals with data parallelism. The processors discussed earlier are all referred to as some type of scalar device. As the name implies, vector processors deal with multiple pieces of data in the context of one instruction. This contrasts with scalar processors, which deal with one piece of data for every instruction. Using Flynn’s taxonomy, these two schemes of dealing with data are generally referred to as SIMD (single instruction, multiple data) and SISD (single instruction, single data), respectively. The great utility in creating CPUs that deal with vectors of data lies in optimizing tasks that tend to require the same operation (for example, a sum or a dot product) to be performed on a large set of data. Some classic examples of these types of tasks are multimedia applications (images, video, and sound), as well as many types of scientific and engineering tasks. Whereas a scalar CPU must complete the entire process of fetching, decoding, and executing each instruction and value in a set of data, a vector CPU can perform a single operation on a comparatively large set of data with one instruction. Of course, this is only possible when the application tends to require many steps which apply one operation to a large set of data.
Most early vector CPUs, such as the Cray-1, were associated almost exclusively with scientific research and cryptography applications. However, as multimedia has largely shifted to digital media, the need for some form of SIMD in general-purpose CPUs has become significant. Shortly after inclusion of floating point execution units started to become commonplace in general-purpose processors, specifications for and implementations of SIMD execution units also began to appear for general-purpose CPUs. Some of these early SIMD specifications like HP’s Multimedia Acceleration eXtensions (MAX) and Intel’s MMX were integer-only. This proved to be a significant impediment for some software developers, since many of the applications that benefit from SIMD primarily deal with floating point numbers. Progressively, these early designs were refined and remade into some of the common, modern SIMD specifications, which are usually associated with one ISA. Some notable modern examples are Intel’s SSE and the PowerPC-related AltiVec (also known as VMX).
The performance or speed of a processor depends on the clock rate (generally given in multiples of hertz) and the instructions per clock (IPC), which together are the factors for the instructions per second (IPS) that the CPU can perform. Many reported IPS values have represented “peak” execution rates on artificial instruction sequences with few branches, whereas realistic workloads consist of a mix of instructions and applications, some of which take longer to execute than others. The performance of the memory hierarchy also greatly affects processor performance, an issue barely considered in MIPS calculations. Because of these problems, various standardized tests, often called “benchmarks” for this purpose—such as SPECint – have been developed to attempt to measure the real effective performance in commonly used applications.
Processing performance of computers is increased by using crash.
 Integrated heat spreader
IHS is usually made of copper covered with a nickel plating.
 See also
- Accelerated Processing Unit
- Addressing mode
- Computer bus
- Computer engineering
- CPU cooling
- CPU core voltage
- CPU design
- CPU power dissipation
- CPU socket
- Digital signal processor
- Execution unit
- Instruction pipeline
- List of CPU architectures
- Ring (computer security)
- Stream processing
- True Performance Index
- Wait state
- . Retrieved May 24, 2012.
- First Draft of a Report on the EDVAC. Moore School of Electrical Engineering, University of Pennsylvania. 1945. http://www.virtualtravelog.net/entries/2003-08-TheFirstDraft.pdf.
- (PDF) Excerpts from A Conversation with Gordon Moore: Moore’s Law. Intel. 2005. ftp://download.intel.com/museum/Moores_Law/Video-Transcripts/Excepts_A_Conversation_with_Gordon_Moore.pdf. Retrieved 2012-07-25.
- Since the program counter counts memory addresses and not instructions, it is incremented by the number of memory units that the instruction word contains. In the case of simple fixed-length instruction word ISAs, this is always the same number. For example, a fixed-length 32-bit instruction word ISA that uses 8-bit memory words would always increment the PC by 4 (except in the case of jumps). ISAs that use variable length instruction words,increment the PC by the number of memory words corresponding to the last instruction’s length.
- Because the instruction set architecture of a CPU is fundamental to its interface and usage, it is often used as a classification of the “type” of CPU. For example, a “PowerPC CPU” uses some variant of the PowerPC ISA. A system can execute a different ISA by running an emulator.
- Some early computers like the Harvard Mark I did not support any kind of “jump” instruction, effectively limiting the complexity of the programs they could run. It is largely for this reason that these computers are often not considered to contain a CPU proper, despite their close similarity as stored program computers.
- The physical concept of voltage is an analog one by its nature, practically having an infinite range of possible values. For the purpose of physical representation of binary numbers, set ranges of voltages are defined as one or zero. These ranges are usually influenced by the circuit designs and operational parameters of the switching elements used to create the CPU, such as a transistor‘s threshold level.
- While a CPU’s integer size sets a limit on integer ranges, this can (and often is) overcome using a combination of software and hardware techniques. By using additional memory, software can represent integers many magnitudes larger than the CPU can. Sometimes the CPU’s ISA will even facilitate operations on integers larger than it can natively represent by providing instructions to make large integer arithmetic relatively quick. While this method of dealing with large integers is somewhat slower than utilizing a CPU with higher integer size, it is a reasonable trade-off in cases where natively supporting the full integer range needed would be cost-prohibitive. See Arbitrary-precision arithmetic for more details on purely software-supported arbitrary-sized integers.
- In fact, all synchronous CPUs use a combination of sequential logic and combinational logic. (See boolean logic)
- Brown, Jeffery (2005). “Application-customized CPU design”. IBM developerWorks. http://www-128.ibm.com/developerworks/power/library/pa-fpfxbox/?ca=dgr-lnxw07XBoxDesign. Retrieved 2005-12-17.
- Garside, J. D., Furber, S. B., & Chung, S-H (1999). AMULET3 Revealed. University of Manchester Computer Science Department. http://www.cs.manchester.ac.uk/apt/publications/papers/async99_A3.php.
- Neither ILP nor TLP is inherently superior over the other; they are simply different means by which to increase CPU parallelism. As such, they both have advantages and disadvantages, which are often determined by the type of software that the processor is intended to run. High-TLP CPUs are often used in applications that lend themselves well to being split up into numerous smaller applications, so-called “embarrassingly parallel problems”. Frequently, a computational problem that can be solved quickly with high TLP design strategies like SMP take significantly more time on high ILP devices like superscalar CPUs, and vice versa.
- . Retrieved 2007-10-06.
- Best-case scenario (or peak) IPC rates in very superscalar architectures are difficult to maintain since it is impossible to keep the instruction pipeline filled all the time. Therefore, in highly superscalar CPUs, average sustained IPC is often discussed rather than peak IPC.
- Earlier the term scalar was used to compare the IPC (instructions per cycle) count afforded by various ILP methods. Here the term is used in the strictly mathematical sense to contrast with vectors. See scalar (mathematics) and Vector (geometric).
- Although SSE/SSE2/SSE3 have superseded MMX in Intel’s general purpose CPUs, later IA-32 designs still support MMX. This is usually accomplished by providing most of the MMX functionality with the same hardware that supports the much more expansive SSE instruction sets.
- “CPU Frequency”. CPU World Glossary. CPU World. 25 March 2008. http://www.cpu-world.com/Glossary/C/CPU_Frequency.html. Retrieved 1 January 2010.
- . Retrieved 1 January 2010.
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