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For example, 32 contiguous bits may be treated as an array of 32 Booleans, a 4-byte string, an unsigned 32-bit integer or an IEEE single precision floating point value. Because the stored bits are never changed, the programmer must know low level details such as representation format, byte order, and alignment needs, to meaningfully cast.
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.
Examples of value types are all primitive types, such as int (a signed 32-bit integer), float (a 32-bit IEEE floating-point number), char (a 16-bit Unicode code unit), decimal (fixed-point numbers useful for handling currency amounts), and System. DateTime (identifies a specific point in time with nanosecond precision).
Real floating-point type, usually referred to as a double-precision floating-point type. Actual properties unspecified (except minimum limits); however, on most systems, this is the IEEE 754 double-precision binary floating-point format (64 bits). This format is required by the optional Annex F "IEC 60559 floating-point arithmetic".
C# 3.0 introduced type inference, allowing the type specifier of a variable declaration to be replaced by the keyword var, if its actual type can be statically determined from the initializer. This reduces repetition, especially for types with multiple generic type-parameters , and adheres more closely to the DRY principle.
Integer addition, for example, can be performed as a single machine instruction, and some offer specific instructions to process sequences of characters with a single instruction. [7] But the choice of primitive data type may affect performance, for example it is faster using SIMD operations and data types to operate on an array of floats.
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.
Double-precision floating-point format (sometimes called FP64 or float64) is a floating-point number format, usually occupying 64 bits in computer memory; it represents a wide range of numeric values by using a floating radix point. Double precision may be chosen when the range or precision of single precision would be insufficient.