Discrete transistor and IC 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 transistor. Transistorized CPUs during the 1950s and 1960s no longer had to be built out of bulky, unreliable, and fragile switching elements like vacuum tubes and electrical relays. With this improvement more complex and reliable CPUs were built onto one or several printed circuit boards containing discrete (individual) components.

Microprocessors

Main article: Microprocessor

The introduction of the microprocessor in the 1970s significantly affected the design and implementation of CPUs. Since the introduction of the first microprocessor (the Intel 4004) in 1970 and the first widely used microprocessor (the Intel 8080) in 1974, this class of CPUs has almost completely overtaken all other central processing unit implementation methods. Mainframe and minicomputer manufacturers of the time launched proprietary IC development programs to upgrade their older computer architectures, and eventually produced instruction set compatible microprocessors that were backward-compatible with their older hardware and software. Combined with the advent and eventual vast success of the now ubiquitous personal computer, the term "CPU" is now applied almost exclusively to microprocessors.

CPU operation

The fundamental operation of most CPUs, regardless of the physical form they take, is to execute a sequence of stored instructions called a program. Discussed here are devices that conform to the common von Neumann architecture. 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 von Neumann CPUs use in their operation: fetch, decode, execute, and writeback.

Design and implementation

Main article: CPU design

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. ^[7]

Clock rate

Main article: Clock rate

Most CPUs, and indeed most sequential logic devices, are synchronous in nature. ^[9] 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.

Parallelism

Main article: Parallel computing

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.

ILP: Instruction pipelining and superscalar architecture

Main articles: Instruction pipelining and Superscalar

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.

TLP: Simultaneous thread execution

Another strategy commonly used to increase the parallelism of CPUs is to include the ability to run multiple threads (programs) at the same time. In general, high-TLP CPUs have been in use much longer than high-ILP ones. Many of the designs pioneered by Cray during the late 1970s and 1980s concentrated on TLP as their primary method of enabling enormous (for the time) computing capability. In fact, TLP in the form of multiple thread execution improvements was in use as early as the 1950s (Smotherman 2005). In the context of single processor design, the two main methodologies used to accomplish TLP are chip-level multiprocessing (CMP) and simultaneous multithreading (SMT). On a higher level, it is very common to build computers with multiple totally independent CPUs in arrangements like symmetric multiprocessing (SMP) and non-uniform memory access (NUMA). ^[13] While using very different means, all of these techniques accomplish the same goal: increasing the number of threads that the CPU(s) can run in parallel.

Vector processors and SIMD

Main articles: Vector processor and SIMD

A less common but increasingly important paradigm of CPUs (and indeed, computing in general) deals with vectors. The processors discussed earlier are all referred to as some type of scalar device. ^[15] 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. These two schemes of dealing with data are generally referred to as SISD (single instruction, single data) and SIMD (single instruction, multiple 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.

Notes