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Starting with Python 2.4, Python's standard library includes a Decimal class in the module decimal. [2] Ruby's standard library includes a BigDecimal class in the module bigdecimal. Java's standard library includes a java.math.BigDecimal class. In Objective-C, the Cocoa and GNUstep APIs provide an NSDecimalNumber class and an NSDecimal C data ...
The binary interchange formats have the "half precision" (16-bit storage format) and "quad precision" (128-bit format) added, together with generalized formulae for some wider formats; the basic formats have 32-bit, 64-bit, and 128-bit encodings. Three new decimal formats are described, matching the lengths of the 32–128-bit binary formats.
All integers with seven or fewer decimal digits, and any 2 n for a whole number −149 ≤ n ≤ 127, can be converted exactly into an IEEE 754 single-precision floating-point value. In the IEEE 754 standard, the 32-bit base-2 format is officially referred to as binary32; it was called single in IEEE 754-1985.
This gives from 33 to 36 significant decimal digits precision. If a decimal string with at most 33 significant digits is converted to the IEEE 754 quadruple-precision format, giving a normal number, and then converted back to a decimal string with the same number of digits, the final result should match the original string.
For the next range, from 2 53 to 2 54, everything is multiplied by 2, so the representable numbers are the even ones, etc. Conversely, for the previous range from 2 51 to 2 52, the spacing is 0.5, etc. The spacing as a fraction of the numbers in the range from 2 n to 2 n+1 is 2 n−52.
For numbers with a base-2 exponent part of 0, i.e. numbers with an absolute value higher than or equal to 1 but lower than 2, an ULP is exactly 2 −23 or about 10 −7 in single precision, and exactly 2 −53 or about 10 −16 in double precision. The mandated behavior of IEEE-compliant hardware is that the result be within one-half of a ULP.
In computing, half precision (sometimes called FP16 or float16) is a binary floating-point computer number format that occupies 16 bits (two bytes in modern computers) in computer memory. It is intended for storage of floating-point values in applications where higher precision is not essential, in particular image processing and neural networks .
For example, the smallest positive number that can be represented in binary64 is 2 −1074; contributions to the −1074 figure include the emin value −1022 and all but one of the 53 significand bits (2 −1022 − (53 − 1) = 2 −1074). Decimal digits is the precision of the format expressed in terms of an equivalent number of decimal digits.