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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.
On some PowerPC systems, [11] long double is implemented as a double-double arithmetic, where a long double value is regarded as the exact sum of two double-precision values, giving at least a 106-bit precision; with such a format, the long double type does not conform to the IEEE floating-point standard.
Type Explanation Size (bits) Format specifier Range Suffix for decimal constants bool: Boolean type, added in C23.: 1 (exact) %d [false, true]char: Smallest addressable unit of the machine that can contain basic character set.
Instead, numeric values of zero are interpreted as false, and any other value is interpreted as true. [9] The newer C99 added a distinct Boolean type _Bool (the more intuitive name bool as well as the macros true and false can be included with stdbool.h), [10] and C++ supports bool as a built-in type and true and false as reserved words. [11]
In single precision, the bias is 127, so in this example the biased exponent is 124; in double precision, the bias is 1023, so the biased exponent in this example is 1020. fraction = .01000… 2 . IEEE 754 adds a bias to the exponent so that numbers can in many cases be compared conveniently by the same hardware that compares signed 2's ...
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).
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The figure below shows the absolute precision for both formats over a range of values. This figure can be used to select an appropriate format given the expected value of a number and the required precision. Precision of binary32 and binary64 in the range 10 −12 to 10 12. An example of a layout for 32-bit floating point is