# Computer number format > Mediated Wiki article. Canonical URL: https://mediated.wiki/source/Computer_number_format > Markdown URL: https://mediated.wiki/source/Computer_number_format.md > Source: https://en.wikipedia.org/wiki/Computer_number_format > Source revision: 1345665294 > License: Creative Commons Attribution-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-sa/4.0/) Internal representation of numeric values in a digital computer This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Find sources: "Computer number format" – news · newspapers · books · scholar · JSTOR (October 2022) (Learn how and when to remove this message) A **computer number format** is the internal representation of numeric values in digital device hardware and software, such as in programmable [computers](/source/Computer) and [calculators](/source/Calculator).[1] Numerical values are stored as groupings of [bits](/source/Bit), such as [bytes](/source/Byte) and words. The encoding between numerical values and bit patterns is chosen for convenience of the operation of the computer;[*[citation needed](https://en.wikipedia.org/wiki/Wikipedia:Citation_needed)*] the encoding used by the computer's instruction set generally requires conversion for external use, such as for printing and display. Different types of processors may have different internal representations of numerical values and different conventions are used for integer and real numbers. Most calculations are carried out with number formats that fit into a processor register, but some software systems allow representation of arbitrarily large numbers using multiple words of memory. ## Binary number representation See also: [Integer (computer science)](/source/Integer_(computer_science)) Computers represent data in sets of binary digits. The representation is composed of bits, which in turn are grouped into larger sets such as bytes. Table 1: Binary to octal Binary string Octal value 000 0 001 1 010 2 011 3 100 4 101 5 110 6 111 7 Table 2: Number of values for a bit string. Length of bit string (b) Number of possible values (N) 1 2 2 4 3 8 4 16 5 32 6 64 7 128 8 256 9 512 10 1024 ... b {\displaystyle b} 2 b = N {\displaystyle 2^{b}=N} A *[bit](/source/Bit)* is a [binary](/source/Binary_numeral_system) [digit](/source/Numerical_digit) that represents one of two [states](/source/State_(computer_science)). The concept of a bit can be understood as a value of either *1* or *0*, *on* or *off*, *yes* or *no*, *true* or *false*, or [encoded](/source/Encoding) by a switch or [toggle](/source/Toggle_switch) of some kind. While a single bit, on its own, is able to represent only two values, a [string of bits](/source/Bit_string) may be used to represent larger values. For example, a string of three bits can represent up to eight distinct values as illustrated in Table 1. As the number of bits composing a string increases, the number of possible *0* and *1* combinations increases [exponentially](/source/Exponentiation). A single bit allows only two value-combinations, two bits combined can make four separate values, three bits for eight, and so on, increasing with the formula 2n. The amount of possible combinations doubles with each binary digit added as illustrated in Table 2. Groupings with a specific number of bits are used to represent varying things and have specific names. A *[byte](/source/Byte)* is a bit string containing the number of bits needed to represent a [character](/source/Character_(computing)). On most modern computers, this is an eight bit string. Because the definition of a byte is related to the number of bits composing a character, some older computers have used a different bit length for their byte.[2] In many [computer architectures](/source/Computer_Architecture), the byte is the smallest [addressable unit](/source/Byte_addressing), the atom of addressability, say. For example, even though 64-bit processors may address memory sixty-four bits at a time, they may still split that memory into eight-bit pieces. This is called byte-addressable memory. Historically, many [CPUs](/source/CPU_core_voltage) read data in some multiple of eight bits.[3] Because the byte size of eight bits is so common, but the definition is not standardized, the term [octet](/source/Octet_(computing)) is sometimes used to explicitly describe an eight bit sequence. A *[nibble](/source/Nibble)* (sometimes *nybble*), is a number composed of four bits.[4] Being a [half-byte](/source/Half-byte), the nibble was named as a play on words. A person may need several nibbles for one bite from something; similarly, a nybble is a part of a byte. Because four bits allow for sixteen values, a nibble is sometimes known as a [hexadecimal digit](/source/Hexadecimal_digit).[5] ## Octal and hexadecimal number display See also: [Base64](/source/Base64) [Octal](/source/Octal) and hexadecimal encoding are convenient ways to represent binary numbers, as used by computers. Computer engineers often need to write out binary quantities, but in practice writing out a binary number such as 1001001101010001 is tedious and prone to errors. Therefore, binary quantities are written in a base-8, or "octal", or, much more commonly, a base-16, "hexadecimal" (*hex*), number format. In the decimal system, there are 10 digits, 0 through 9, which combine to form numbers. In an octal system, there are only 8 digits, 0 through 7. That is, the value of an octal "10" is the same as a decimal "8", an octal "20" is a decimal "16", and so on. In a hexadecimal system, there are 16 digits, 0 through 9 followed, by convention, with A through F. That is, a hexadecimal "10" is the same as a decimal "16" and a hexadecimal "20" is the same as a decimal "32". An example and comparison of numbers in different bases is described in the chart below. When typing numbers, formatting characters are used to describe the number system, for example 000_0000B or 0b000_00000 for binary and 0F8H or 0xf8 for hexadecimal numbers. ### Converting between bases Table 3: Comparison of values in different bases Decimal Binary Octal Hexadecimal 0 000000 00 00 1 000001 01 01 2 000010 02 02 3 000011 03 03 4 000100 04 04 5 000101 05 05 6 000110 06 06 7 000111 07 07 8 001000 10 08 9 001001 11 09 10 001010 12 0A 11 001011 13 0B 12 001100 14 0C 13 001101 15 0D 14 001110 16 0E 15 001111 17 0F Main article: [Positional notation (base conversion)](/source/Positional_notation#Base_conversion) Each of these number systems is a positional system, but while decimal weights are powers of 10, the octal weights are powers of 8 and the hexadecimal weights are powers of 16. To convert from hexadecimal or octal to decimal, for each digit one multiplies the value of the digit by the value of its position and then adds the results. For example: - octal 756 = ( 7 × 8 2 ) + ( 5 × 8 1 ) + ( 6 × 8 0 ) = ( 7 × 64 ) + ( 5 × 8 ) + ( 6 × 1 ) = 448 + 40 + 6 = decimal 494 hex 3 b 2 = ( 3 × 16 2 ) + ( 11 × 16 1 ) + ( 2 × 16 0 ) = ( 3 × 256 ) + ( 11 × 16 ) + ( 2 × 1 ) = 768 + 176 + 2 = decimal 946 {\displaystyle {\begin{aligned}&{\text{octal }}756\\[5pt]={}&(7\times 8^{2})+(5\times 8^{1})+(6\times 8^{0})\\[5pt]={}&(7\times 64)+(5\times 8)+(6\times 1)\\[5pt]={}&448+40+6\\[5pt]={}&{\text{decimal }}494\end{aligned}}\qquad {\begin{aligned}&{\text{hex }}\mathrm {3b2} \\[5pt]={}&(3\times 16^{2})+(11\times 16^{1})+(2\times 16^{0})\\[5pt]={}&(3\times 256)+(11\times 16)+(2\times 1)\\[5pt]={}&768+176+2\\[5pt]={}&{\text{decimal }}946\end{aligned}}} ## Representing fractions in binary ### Fixed-point numbers [Fixed-point](/source/Fixed-point_arithmetic) formatting can be useful to represent fractions in binary. The number of bits needed for the precision and range desired must be chosen to store the fractional and integer parts of a number. For instance, using a 32-bit format, 16 bits may be used for the integer and 16 for the fraction. The eight's bit is followed by the four's bit, then the two's bit, then the one's bit. The fractional bits continue the pattern set by the integer bits. The next bit is the half's bit, then the quarter's bit, then the eighth's bit, and so on. For example: integer bits fractional bits 0.500 = ⁠1/2⁠ = 00000000 00000000.10000000 00000000 1.250 = ⁠1+1/4⁠ = 00000000 00000001.01000000 00000000 7.375 = ⁠7+3/8⁠ = 00000000 00000111.01100000 00000000 This form of encoding cannot represent some values in binary. For example, the fraction ⁠1/5⁠, 0.2 in decimal, the closest approximations would be as follows: 13107 / 65536 = 00000000 00000000.00110011 00110011 = 0.1999969... in decimal 13108 / 65536 = 00000000 00000000.00110011 00110100 = 0.2000122... in decimal Even if more digits are used, an exact representation is impossible. The number ⁠1/3⁠, written in decimal as 0.333333333..., continues indefinitely. If prematurely terminated, the value would not represent ⁠1/3⁠ precisely. ### Floating-point numbers While both unsigned and signed integers are used in digital systems, even a 32-bit integer is not enough to handle all the range of numbers a calculator can handle, and that's not even including fractions. To approximate the greater range and precision of [real numbers](/source/Real_number), we have to abandon signed integers and fixed-point numbers and go to a "[floating-point](/source/Floating-point_arithmetic)" format. In the decimal system, we are familiar with floating-point numbers of the form ([scientific notation](/source/Scientific_notation)): - 1.1030402 × 105 = 1.1030402 × 100000 = 110304.02 or, more compactly: - 1.1030402E5 which means "1.1030402 times 1 followed by 5 zeroes". We have a certain numeric value (1.1030402) known as a "[significand](/source/Significand)", multiplied by a power of 10 (E5, meaning 105 or 100,000), known as an "[exponent](/source/Exponentiation)". If we have a negative exponent, that means the number is multiplied by a 1 that many places to the right of the decimal point. For example: - 2.3434E−6 = 2.3434 × 10−6 = 2.3434 × 0.000001 = 0.0000023434 The advantage of this scheme is that by using the exponent we can get a much wider range of numbers, even if the number of digits in the significand, or the "numeric precision", is much smaller than the range. Similar binary floating-point formats can be defined for computers. There is a number of such schemes, the most popular has been defined by [Institute of Electrical and Electronics Engineers](/source/Institute_of_Electrical_and_Electronics_Engineers) (IEEE). The [IEEE 754-2008](/source/IEEE_floating_point) standard specification defines a 64 bit floating-point format with: - an 11-bit binary exponent, using "excess-1023" format. Excess-1023 means the exponent appears as an unsigned binary integer from 0 to 2047; subtracting 1023 gives the actual signed value - a 52-bit significand, also an unsigned binary number, defining a fractional value with a leading implied "1" - a sign bit, giving the sign of the number. With the bits stored in 8 bytes of memory: byte 0 S x10 x9 x8 x7 x6 x5 x4 byte 1 x3 x2 x1 x0 m51 m50 m49 m48 byte 2 m47 m46 m45 m44 m43 m42 m41 m40 byte 3 m39 m38 m37 m36 m35 m34 m33 m32 byte 4 m31 m30 m29 m28 m27 m26 m25 m24 byte 5 m23 m22 m21 m20 m19 m18 m17 m16 byte 6 m15 m14 m13 m12 m11 m10 m9 m8 byte 7 m7 m6 m5 m4 m3 m2 m1 m0 where "S" denotes the sign bit, "x" denotes an exponent bit, and "m" denotes a significand bit. Once the bits here have been extracted, they are converted with the computation: - × (1 + ) × 2 − 1023 This scheme provides numbers valid out to about 15 decimal digits, with the following range of numbers: maximum minimum positive 1.797693134862231E+308 4.940656458412465E-324 negative -4.940656458412465E-324 -1.797693134862231E+308 The specification also defines several special values that are not defined numbers, and are known as *[NaNs](/source/NaN)*, for "Not A Number". These are used by programs to designate invalid operations and the like. Some programs also use 32-bit floating-point numbers. The most common scheme uses a 23-bit significand with a sign bit, plus an 8-bit exponent in "excess-127" format, giving seven valid decimal digits. byte 0 S x7 x6 x5 x4 x3 x2 x1 byte 1 x0 m22 m21 m20 m19 m18 m17 m16 byte 2 m15 m14 m13 m12 m11 m10 m9 m8 byte 3 m7 m6 m5 m4 m3 m2 m1 m0 The bits are converted to a numeric value with the computation: - × (1 + ) × 2 − 127 leading to the following range of numbers: maximum minimum positive 3.402823E+38 2.802597E-45 negative -2.802597E-45 -3.402823E+38 Such floating-point numbers are known as "reals" or "floats" in general, but with a number of variations: A 32-bit float value is sometimes called a "real32" or a "single", meaning "single-precision floating-point value". A 64-bit float is sometimes called a "real64" or a "double", meaning "double-precision floating-point value". The relation between numbers and bit patterns is chosen for convenience in computer manipulation; eight bytes stored in computer memory may represent a 64-bit real, two 32-bit reals, or four signed or unsigned integers, or some other kind of data that fits into eight bytes. The only difference is how the computer interprets them. If the computer stored four unsigned integers and then read them back from memory as a 64-bit real, it almost always would be a perfectly valid real number, though it would be junk data. Only a finite range of real numbers can be represented with a given number of bits. Arithmetic operations can overflow or underflow, producing a value too large or too small to be represented. The representation has a limited precision. For example, only 15 decimal digits can be represented with a 64-bit real. If a very small floating-point number is added to a large one, the result is just the large one. The small number was too small to even show up in 15 or 16 digits of resolution, and the computer effectively discards it. Analyzing the effect of limited precision is a well-studied problem. Estimates of the magnitude of round-off errors and methods to limit their effect on large calculations are part of any large computation project. The precision limit is different from the range limit, as it affects the significand, not the exponent. The significand is a binary fraction that doesn't necessarily perfectly match a decimal fraction. In many cases a sum of reciprocal powers of 2 does not match a specific decimal fraction, and the results of computations will be slightly off. For example, the decimal fraction "0.1" is equivalent to an infinitely repeating binary fraction: 0.000110011 ...[6] ## Numbers in programming languages This section needs additional citations for verification. Please help improve this article by adding citations to reliable sources in this section. Unsourced material may be challenged and removed. Find sources: "Computer number format" – news · newspapers · books · scholar · JSTOR (December 2018) (Learn how and when to remove this message) Programming in [assembly language](/source/Assembly_language) requires the programmer to keep track of the representation of numbers. Where the processor does not support a required mathematical operation, the programmer must work out a suitable algorithm and instruction sequence to carry out the operation; on some microprocessors, even integer multiplication must be done in software. High-level [programming languages](/source/Programming_language) such as [Ruby](/source/Ruby_(programming_language)) and [Python](/source/Python_(programming_language)) offer an abstract number that may be an expanded type such as *rational*, *bignum*, or *complex*. Mathematical operations are carried out by library routines provided by the implementation of the language. A given mathematical symbol in the source code, by [operator overloading](/source/Operator_overloading), will invoke different object code appropriate to the representation of the numerical type; mathematical operations on any number—whether signed, unsigned, rational, floating-point, fixed-point, integral, or complex—are written exactly the same way. Some languages, such as [REXX](/source/REXX) and [Java](/source/Java_(programming_language)), provide decimal floating-points operations, which provide rounding errors of a different form. ## See also - [Arbitrary-precision arithmetic](/source/Arbitrary-precision_arithmetic) - [Binary-coded decimal](/source/Binary-coded_decimal) - [Binary-to-text encoding](/source/Binary-to-text_encoding) - [Binary number](/source/Binary_number) - [Gray code](/source/Gray_code) - [Numeral system](/source/Numeral_system) - [Unum](/source/Unum_(number_format)) - [Posit](/source/Posit_(number_format)) ## Notes and references *The initial version of this article was based on a [public domain](/source/Public_domain) article from [Greg Goebel's Vectorsite](https://vc.airvectors.net/index.html).* 1. **[^](#cite_ref-1)** Jon Stokes (2007). [*Inside the machine: an illustrated introduction to microprocessors and computer architecture*](https://books.google.com/books?id=Q1zSIarI8xoC&pg=PA66). No Starch Press. p. 66. [ISBN](/source/ISBN_(identifier)) [978-1-59327-104-6](https://en.wikipedia.org/wiki/Special:BookSources/978-1-59327-104-6). 1. **[^](#cite_ref-2)** ["byte definition"](http://catb.org/~esr/jargon/html/B/byte.html). Retrieved 24 April 2012. 1. **[^](#cite_ref-3)** ["Microprocessor and CPU (Central Processing Unit)"](https://web.archive.org/web/20171003225434/http://www.networkdictionary.com/hardware/mc.php). Network Dictionary. Archived from [the original](http://www.networkdictionary.com/hardware/mc.php) on 3 October 2017. Retrieved 1 May 2012. 1. **[^](#cite_ref-4)** ["nybble definition"](http://catb.org/~esr/jargon/html/N/nybble.html). Retrieved 3 May 2012. 1. **[^](#cite_ref-5)** ["Nybble"](http://www.techterms.com/definition/nybble). TechTerms.com. Retrieved 3 May 2012. 1. **[^](#cite_ref-6)** Goebel, Greg. ["Computer Numbering Format"](http://www.vectorsite.net/tsfloat.html). 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