In computing, quadruple precision (or quad precision) is a binary floating-point-based computer number format that occupies 16 bytes (128 bits) in 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,^{[1]} 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."^{[2]}
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.^{[3]}
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: log_{10}(2^{113}) ≈ 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 0000_{16} and 7FFF_{16} are interpreted specially.
Exponent | Significand zero | Significand non-zero | Equation |
---|---|---|---|
0000_{16} | 0, −0 | subnormal numbers | (−1)^{signbit} × 2^{−16382} × 0.significandbits_{2} |
0001_{16}, ..., 7FFE_{16} | normalized value | (−1)^{signbit} × 2^{exponentbits2 − 16383} × 1.significandbits_{2} | |
7FFF_{16} | ±∞ | 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 2^{16384} − 2^{16271} ≈ 1.1897 × 10^{4932}.
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 0001_{16} = 2^{−16382} × 2^{−112} = 2^{−16494} ≈ 6.4751751194380251109244389582276465525 × 10^{−4966} (smallest positive subnormal number)
0000 ffff ffff ffff ffff ffff ffff ffff_{16} = 2^{−16382} × (1 − 2^{−112}) ≈ 3.3621031431120935062626778173217519551 × 10^{−4932} (largest subnormal number)
0001 0000 0000 0000 0000 0000 0000 0000_{16} = 2^{−16382} ≈ 3.3621031431120935062626778173217526026 × 10^{−4932} (smallest positive normal number)
7ffe ffff ffff ffff ffff ffff ffff ffff_{16} = 2^{16383} × (2 − 2^{−112}) ≈ 1.1897314953572317650857593266280070162 × 10^{4932} (largest normal number)
3ffe ffff ffff ffff ffff ffff ffff ffff_{16} = 1 − 2^{−113} ≈ 0.9999999999999999999999999999999999037 (largest number less than one)
3fff 0000 0000 0000 0000 0000 0000 0000_{16} = 1 (one)
3fff 0000 0000 0000 0000 0000 0000 0001_{16} = 1 + 2^{−112} ≈ 1.0000000000000000000000000000000001926 (smallest number larger than one)
c000 0000 0000 0000 0000 0000 0000 0000_{16} = −2
0000 0000 0000 0000 0000 0000 0000 0000_{16} = 0 8000 0000 0000 0000 0000 0000 0000 0000_{16} = −0
7fff 0000 0000 0000 0000 0000 0000 0000_{16} = infinity ffff 0000 0000 0000 0000 0000 0000 0000_{16} = −infinity
4000 921f b544 42d1 8469 898c c517 01b8_{16} ≈ π
3ffd 5555 5555 5555 5555 5555 5555 5555_{16} ≈ 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.^{[4]}^{[5]}^{[6]} Using pairs of IEEE double-precision values with 53-bit significands, double-double arithmetic can represent operations with at least^{[4]} a 2×53=106-bit significand (actually 107 bits^{[7]} 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,^{[4]} significantly lower than the 15-bit exponent of IEEE quadruple precision (a range of 1.8 × 10^{308} for double-double versus 1.2 × 10^{4932} 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.^{[5]} 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.^{[4]}^{[5]}
Note that double-double arithmetic has the following special characteristics:^{[8]}
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.^{[9]}
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 real(real128)
(module 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
. (Quadruple-precision REAL*16
is supported by the Intel Fortran Compiler^{[10]} and by the GNU Fortran compiler^{[11]} 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^{[12]} and the Intel C++ compiler with a /Qlong‑double
switch^{[13]}) or simply as being synonymous with double precision (e.g. Microsoft Visual C++^{[14]}), 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^{[15]}^{[16]}^{[17]}) and SPARC,^{[18]} or the Sun Studio compilers on SPARC.^{[19]} 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,^{[20]} and on PowerPC as IEEE 128-bit floating-point using the -mfloat128-hardware or -mfloat128 options;^{[21]} and some versions of Intel's C/C++ compiler for x86 and x86-64 supply a nonstandard quadruple-precision type called _Quad
.^{[22]}
__float128
and __complex128
operations.__float128
and _Quad
types, and includes a custom implementation of the standard math library.^{[23]}The IBM POWER9 CPU (ISA 3.0) has native 128-bit hardware support.^{[21]}
Native support of IEEE 128-bit floats is defined in PA-RISC 1.0,^{[26]} and in SPARC V8^{[27]} and V9^{[28]} architectures (e.g. there are 16 quad-precision registers %q0, %q4, ...), but no SPARC CPU implements quad-precision operations in hardware as of 2004.^{[29]}
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,^{[30]} -195, and others by special request or simulated by OS software). IEEE quadruple precision was added to the S/390 G5 in 1998,^{[31]} and is supported in hardware in subsequent z/Architecture processors.^{[32]}^{[33]}
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).
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