In computing, quadruple precision (or quad precision) is a binary floating point–based computer number format that occupies 16 bytes (128 bits) with precision more than twice the 53-bit double precision.
This 128-bit quadruple precision is designed not only for applications requiring results in higher than double precision, but also, as a primary function, to allow the computation of double precision results more reliably and accurately by minimising overflow and round-off errors in intermediate calculations and scratch variables. William Kahan, primary architect of the original IEEE-754 floating point standard noted, "For now the 10-byte Extended format is a tolerable compromise between the value of extra-precise arithmetic and the price of implementing it to run fast; very soon two more bytes of precision will become tolerable, and ultimately a 16-byte format ... That kind of gradual evolution towards wider precision was already in view when IEEE Standard 754 for Floating-Point Arithmetic was framed."
In IEEE 754-2008 the 128-bit base-2 format is officially referred to as binary128.
The IEEE 754 standard specifies a binary128 as having:
This gives from 33 to 36 significant decimal digits precision. If a decimal string with at most 33 significant digits is converted to IEEE 754 quadruple-precision representation, and then converted back to a decimal string with the same number of digits, the final result should match the original string. If an IEEE 754 quadruple-precision number is converted to a decimal string with at least 36 significant digits, and then converted back to quadruple-precision representation, the final result must match the original number.
The format is written with an implicit lead bit with value 1 unless the exponent is stored with all zeros. Thus only 112 bits of the significand appear in the memory format, but the total precision is 113 bits (approximately 34 decimal digits: log10(2113) ≈ 34.016). The bits are laid out as:
A binary256 would have a significand precision of 237 bits (approximately 71 decimal digits) and exponent bias 262143.
The quadruple-precision binary floating-point exponent is encoded using an offset binary representation, with the zero offset being 16383; this is also known as exponent bias in the IEEE 754 standard.
Thus, as defined by the offset binary representation, in order to get the true exponent, the offset of 16383 has to be subtracted from the stored exponent.
The stored exponents 000016 and 7FFF16 are interpreted specially.
|Exponent||Significand zero||Significand non-zero||Equation|
|000016||0, −0||subnormal numbers||(−1)signbit × 2−16382 × 0.significandbits2|
|000116, ..., 7FFE16||normalized value||(−1)signbit × 2exponentbits2 − 16383 × 1.significandbits2|
|7FFF16||±∞||NaN (quiet, signalling)|
The minimum strictly positive (subnormal) value is 2−16494 ≈ 10−4965 and has a precision of only one bit. The minimum positive normal value is 2−16382 ≈ 3.3621 × 10−4932 and has a precision of 113 bits, i.e. ±2−16494 as well. The maximum representable value is 216384 − 216271 ≈ 1.1897 × 104932.
These examples are given in bit representation, in hexadecimal, of the floating-point value. This includes the sign, (biased) exponent, and significand.
0000 0000 0000 0000 0000 0000 0000 000116 = 2−16382 × 2−112 = 2−16494 ≈ 6.4751751194380251109244389582276465525 × 10−4966 (smallest positive subnormal number)
0000 ffff ffff ffff ffff ffff ffff ffff16 = 2−16382 × (1 − 2−112) ≈ 3.3621031431120935062626778173217519551 × 10−4932 (largest subnormal number)
0001 0000 0000 0000 0000 0000 0000 000016 = 2−16382 ≈ 3.3621031431120935062626778173217526026 × 10−4932 (smallest positive normal number)
7ffe ffff ffff ffff ffff ffff ffff ffff16 = 216383 × (2 − 2−112) ≈ 1.1897314953572317650857593266280070162 × 104932 (largest normal number)
3ffe ffff ffff ffff ffff ffff ffff ffff16 = 1 − 2−113 ≈ 0.9999999999999999999999999999999999037 (largest number less than one)
3fff 0000 0000 0000 0000 0000 0000 000016 = 1 (one)
3fff 0000 0000 0000 0000 0000 0000 000116 = 1 + 2−112 ≈ 1.0000000000000000000000000000000001926 (smallest number larger than one)
c000 0000 0000 0000 0000 0000 0000 000016 = −2
0000 0000 0000 0000 0000 0000 0000 000016 = 0 8000 0000 0000 0000 0000 0000 0000 000016 = −0
7fff 0000 0000 0000 0000 0000 0000 000016 = infinity ffff 0000 0000 0000 0000 0000 0000 000016 = −infinity
4000 921f b544 42d1 8469 898c c517 01b816 ≈ π
3ffd 5555 5555 5555 5555 5555 5555 555516 ≈ 1/3
By default, 1/3 rounds down like double precision, because of the odd number of bits in the significand.
So the bits beyond the rounding point are
0101... which is less than 1/2 of a unit in the last place.
A common software technique to implement nearly quadruple precision using pairs of double-precision values is sometimes called double-double arithmetic. Using pairs of IEEE double-precision values with 53-bit significands, double-double arithmetic can represent operations with at least a 2×53=106-bit significand (actually 107 bits except for some of the largest values, due to the limited exponent range), only slightly less precise than the 113-bit significand of IEEE binary128 quadruple precision. The range of a double-double remains essentially the same as the double-precision format because the exponent has still 11 bits, significantly lower than the 15-bit exponent of IEEE quadruple precision (a range of 1.8 × 10308 for double-double versus 1.2 × 104932 for binary128).
In particular, a double-double/quadruple-precision value q in the double-double technique is represented implicitly as a sum q = x + y of two double-precision values x and y, each of which supplies half of q's significand. That is, the pair (x, y) is stored in place of q, and operations on q values (+, −, ×, ...) are transformed into equivalent (but more complicated) operations on the x and y values. Thus, arithmetic in this technique reduces to a sequence of double-precision operations; since double-precision arithmetic is commonly implemented in hardware, double-double arithmetic is typically substantially faster than more general arbitrary-precision arithmetic techniques.
Note that double-double arithmetic has the following special characteristics:
In addition to the double-double arithmetic, it is also possible to generate triple-double or quad-double arithmetic if higher precision is required without any higher precision floating-point library. They are represented as a sum of three (or four) double-precision values respectively. They can represent operations with at least 159/161 and 212/215 bits respectively.
A similar technique can be used to produce a double-quad arithmetic, which is represented as a sum of two quadruple-precision values. They can represent operations with at least 226 (or 227) bits.
Quadruple precision is often implemented in software by a variety of techniques (such as the double-double technique above, although that technique does not implement IEEE quadruple precision), since direct hardware support for quadruple precision is, as of 2016, less common (see "Hardware support" below). One can use general arbitrary-precision arithmetic libraries to obtain quadruple (or higher) precision, but specialized quadruple-precision implementations may achieve higher performance.
A separate question is the extent to which quadruple-precision types are directly incorporated into computer programming languages.
Quadruple precision is specified in Fortran by the
iso_fortran_env from Fortran 2008 must be used, the constant
real128 is equal to 16 on most processors), or as
real(selected_real_kind(33, 4931)), or in a non-standard way as
REAL*16 is supported by the Intel Fortran Compiler and by the GNU Fortran compiler on x86, x86-64, and Itanium architectures, for example.)
In C/C++ with a few systems and compilers, quadruple precision may be specified by the long double type, but this is not required by the language (which only requires
long double to be at least as precise as
double), nor is it common. On x86 and x86-64, the most common C/C++ compilers implement
long double as either 80-bit extended precision (e.g. the GNU C Compiler gcc and the Intel C++ compiler with a
/Qlong‑double switch) or simply as being synonymous with double precision (e.g. Microsoft Visual C++), rather than as quadruple precision. On a few other architectures, some C/C++ compilers implement
long double as quadruple precision, e.g. gcc on PowerPC (as double-double) and SPARC, or the Sun Studio compilers on SPARC. Even if
long double is not quadruple precision, however, some C/C++ compilers provide a nonstandard quadruple-precision type as an extension. For example, gcc provides a quadruple-precision type called
__float128 for x86, x86-64 and Itanium CPUs, and on PowerPC as IEEE 128-bit floating-point using the -mfloat128-hardware or -mfloat128 options; and some versions of Intel's C/C++ compiler for x86 and x86-64 supply a nonstandard quadruple-precision type called
_Quadtypes, and includes a custom implementation of the standard math library.
Native support of IEEE 128-bit floats is defined in PA-RISC 1.0, and in SPARC V8 and V9 architectures (e.g. there are 16 quad-precision registers %q0, %q4, ...), but no SPARC CPU implements quad-precision operations in hardware as of 2004.
Non-IEEE extended-precision (128 bit of storage, 1 sign bit, 7 exponent bit, 112 fraction bit, 8 bits unused) was added to the IBM System/370 series (1970s–1980s) and was available on some S/360 models in the 1960s (S/360-85, -195, and others by special request or simulated by OS software). IEEE quadruple precision was added to the S/390 G5 in 1998, and is supported in hardware in subsequent z/Architecture processors.
The VAX processor implemented non-IEEE quadruple-precision floating point as its "H Floating-point" format. It had one sign bit, a 15-bit exponent and 112-fraction bits, however the layout in memory was significantly different from IEEE quadruple precision and the exponent bias also differed. Only a few of the earliest VAX processors implemented H Floating-point instructions in hardware, all the others emulated H Floating-point in software.
Quadruple-precision (128-bit) hardware implementation should not be confused with "128-bit FPUs" that implement SIMD instructions, such as Streaming SIMD Extensions or AltiVec, which refers to 128-bit vectors of four 32-bit single-precision or two 64-bit double-precision values that are operated on simultaneously.
SPARC is an instruction set architecture (ISA) with 32-bit integer and 32-, 64-, and 128-bit IEEE Standard 754 floating-point as its principal data types.
Floating-point: The architecture provides an IEEE 754-compatible floating-point instruction set, operating on a separate register file that provides 32 single-precision (32-bit), 32 double-precision (64-bit), 16 quad-precision (128-bit) registers, or a mixture thereof.
There are four situations, however, when the hardware will not successfully complete a floating-point instruction: ... The instruction is not implemented by the hardware (such as ... quad-precision instructions on any SPARC FPU).
In the C programming language, data types are declarations for memory locations or
variables that determine the characteristics of the data that may be stored and the methods (operations) of processing that are permitted involving them.
The C language provides basic arithmetic types, such as integer and real number types, and syntax to build array and compound types. Several headers in the C standard library contain definitions of support types, that have additional properties, such as providing storage with an exact size, independent of the implementation.IBM hexadecimal floating point
IBM System/360 computers, and subsequent machines based on that architecture (mainframes), support a hexadecimal floating-point format (HFP).In comparison to IEEE 754 floating-point, the IBM floating-point format has a longer significand, and a shorter exponent. All IBM floating-point formats have 7 bits of exponent with a bias of 64. The normalized range of representable numbers is from 16−65 to 1663 (approx. 5.39761 × 10−79 to 7.237005 × 1075).
The number is represented as the following formula: (−1)sign × 0.significand × 16exponent−64.Orders of magnitude (numbers)
This list contains selected positive numbers in increasing order, including counts of things, dimensionless quantity and probabilities. Each number is given a name in the short scale, which is used in English-speaking countries, as well as a name in the long scale, which is used in some of the countries that do not have English as their national language.Precision (computer science)
In computer science, the precision of a numerical quantity is a measure of the detail in which the quantity is expressed. This is usually measured in bits, but sometimes in decimal digits. It is related to precision in mathematics, which describes the number of digits that are used to express a value.
Some of the standardized precision formats are
Half-precision floating-point format
Single-precision floating-point format
Double-precision floating-point format
Quadruple-precision floating-point format
Octuple-precision floating-point formatOf these, octuple-precision format is rarely used. The single- and double-precision formats are most widely used and supported on nearly all platforms. The use of half-precision format has been increasing especially in the field of machine learning since many machine learning algorithms are inherently error-tolerant.Quadruple
Quadruple may refer to:
4-tuple, an ordered list of elements, with four elements
Quad (figure skating), a figure skating jump
Quadruple (computing), an term used as alternative for nibble in some contexts
Quadruple-precision floating-point format in computing
Multiple birth with four offspring
A term for winning four football trophies in a single season
See also platform-dependent and independent units of information