Difference between Single Precision and Double Precision
According to IEEE standard, floating-point number is represented in two ways:
Precision | Base | Sign | Exponent | Significand |
Single precision | 2 | 1 | 8 | 23+1 |
Double precision | 2 | 1 | 11 | 52+1 |
1. Single Precision: Single Precision is a format proposed by IEEE for the representation of floating-point numbers. It occupies 32 bits in computer memory.
2. Double Precision: Double Precision is also a format given by IEEE for the representation of the floating-point number. It occupies 64 bits in computer memory.
Difference between Single and Double Precision:
SINGLE PRECISION | DOUBLE PRECISION |
---|---|
In single precision, 32 bits are used to represent floating-point number. | In double precision, 64 bits are used to represent floating-point number. |
This format, also known as FP32, is suitable for calculations that won’t be adversely affected by some approximation. | This format, often known as FP64, is suitable to represent values that need a wider range or more exact computations. |
It uses 8 bits for exponent. | It uses 11 bits for exponent. |
In single precision, 23 bits are used for mantissa. | In double precision, 52 bits are used for mantissa. |
Bias number is 127. | Bias number is 1023. |
Range of numbers in single precision : 2^(-126) to 2^(+127) | Range of numbers in double precision : 2^(-1022) to 2^(+1023) |
This is used where precision matters less. | This is used where precision matters more. |
It is used for wide representation. | It is used for minimization of approximation. |
It is used in simple programs like games. | It is used in complex programs like scientific calculator. |
This is called binary32. | This is called binary64. |
It requires fewer resources as compared to double precision. | It provides more accurate results but at the cost of greater computational power, memory space, and data transfer. |
It is less expensive. | The cost incurred using this format does not always justify its use for every computation . |
Please refer Floating Point Representation for details.
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