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Before the widespread adoption of IEEE 754-1985, the representation and properties of floating-point data types depended on the computer manufacturer and computer model, and upon decisions made by programming-language designers. E.g., GW-BASIC's single-precision data type was the 32-bit MBF floating-point format.
strictfp, an obsolete keyword in the Java programming language that previously restricted arithmetic to IEEE 754 single and double precision to ensure reproducibility across common hardware platforms (as of Java 17, this behavior is required) Table-maker's dilemma for more about the correct rounding of functions; Standard Apple Numerics Environment
Programming languages that support arbitrary precision computations, either built-in, or in the standard library of the language: Ada: the upcoming Ada 202x revision adds the Ada.Numerics.Big_Numbers.Big_Integers and Ada.Numerics.Big_Numbers.Big_Reals packages to the standard library, providing arbitrary precision integers and real numbers.
The "decimal" data type of the C# and Python programming languages, and the decimal formats of the IEEE 754-2008 standard, are designed to avoid the problems of binary floating-point representations when applied to human-entered exact decimal values, and make the arithmetic always behave as expected when numbers are printed in decimal.
The Go programming language has built-in types complex64 (each component is 32-bit float) and complex128 (each component is 64-bit float). Imaginary number literals can be specified by appending an "i". The Perl core module Math::Complex provides support for complex numbers. Python provides the built-in complex type. Imaginary number literals ...
float and double, floating-point numbers with single and double precisions; boolean, a Boolean type with logical values true and false; returnAddress, a value referring to an executable memory address. This is not accessible from the Java programming language and is usually left out. [13] [14]
This was subsequently addressed in IEEE 754-2008, which standardized the encoding of decimal floating-point data, albeit with two different alternative methods. IBM POWER6 and newer POWER processors include DFP in hardware, as does the IBM System z9 [5] (and later zSeries machines). SilMinds offers SilAx, a configurable vector DFP coprocessor. [6]
Internally, Caché stores data in multidimensional arrays capable of carrying hierarchically structured data.These are the same “global” data structures used by the MUMPS programming language, which influenced the design of Caché, and are similar to those used by MultiValue (also known as PICK) systems.