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This can express values in the range ±65,504, with the minimum value above 1 being 1 + 1/1024. Depending on the computer, half-precision can be over an order of magnitude faster than double precision, e.g. 550 PFLOPS for half-precision vs 37 PFLOPS for double precision on one cloud provider. [1]
The value distribution is similar to floating point, but the value-to-representation curve (i.e., the graph of the logarithm function) is smooth (except at 0). Conversely to floating-point arithmetic, in a logarithmic number system multiplication, division and exponentiation are simple to implement, but addition and subtraction are complex.
→ value load a float value from local variable 1 fload_2 24 0010 0100 → value load a float value from local variable 2 fload_3 25 0010 0101 → value load a float value from local variable 3 fmul 6a 0110 1010 value1, value2 → result multiply two floats fneg 76 0111 0110 value → result negate a float frem 72 0111 0010 value1, value2 → ...
A floating-point variable can represent a wider range of numbers than a fixed-point variable of the same bit width at the cost of precision. A signed 32-bit integer variable has a maximum value of 2 31 − 1 = 2,147,483,647, whereas an IEEE 754 32-bit base-2 floating-point variable has a maximum value of (2 − 2 −23) × 2 127 ≈ 3.4028235 ...
byte, short, int, long, char (integer types with a variety of ranges) 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 ...
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.
Such floating-point numbers are known as "reals" or "floats" in general, but with a number of variations: A 32-bit float value is sometimes called a "real32" or a "single", meaning "single-precision floating-point value". A 64-bit float is sometimes called a "real64" or a "double", meaning "double-precision floating-point value".
The finite positive and finite negative numbers furthest from zero (represented by the value with 2046 in the Exp field and all 1s in the fraction field) are ±(2−2 −52) × 2 1023 [5] ≈ ±1.79769 × 10 308; Some example range and gap values for given exponents in double precision: