Why is float single precision?

Because float occupies a relatively small storage space, only 32 bits. The reasons why float is designed as single precision: 1. Storage space is a limited resource. Using smaller storage space to store floating point numbers can provide greater flexibility and efficiency. The 32-bit storage space of single precision floating point numbers is enough. Meet the needs of most applications; 2. Computational efficiency is also one of the factors to consider. When performing floating-point calculations, using smaller data types can improve calculation speed. Single-precision floating-point numbers only require 32-bit calculation operations.

# Operating system for this tutorial: Windows 10 system, Dell G3 computer.

Float is a data type used to store floating point numbers (i.e. decimals). It is called "single precision" because it occupies a relatively small storage space, only 32 bits (4 bytes), while "double precision" refers to another floating point data type double, which occupies The space is 64 bits (8 bytes).

Why is float designed for single precision, rather than double precision or other larger precision? This mainly involves two considerations: storage space and computing efficiency.

Storage space is a limited resource, especially in computer memory. Each additional storage space for a data type will occupy more memory, limiting the amount of data that can be stored simultaneously. In many applications, storage space is a critical factor, so using smaller storage space to store floating point numbers can provide greater flexibility and efficiency. The 32-bit storage space of single-precision floating point numbers is sufficient to meet the needs of most applications.

Computing efficiency is also one of the factors to consider. When performing floating point calculations, using smaller data types can increase calculation speed. Single-precision floating-point numbers only require 32-bit calculation operations, which can be completed faster than the 64-bit operations of double-precision floating-point numbers. For some applications that require efficient computing, such as scientific computing, image processing, and game development, computing speed is crucial.

Single-precision floating point numbers also have some limitations. Since there is only 32 storage spaces, its representation range and accuracy are relatively small. The number of significant digits it can represent is approximately 6-7 digits, while the number of significant digits that a double precision floating point number can represent is approximately 15-16 digits. This means that double-precision floating point numbers may be more suitable when working with large ranges of values ​​or calculations that require higher precision.

In short, the design of float as a single-precision floating point number is based on the balance between storage space and calculation efficiency. It provides sufficient accuracy and range for most applications and is more computationally efficient. In specific application scenarios, appropriate data types can be selected according to needs to balance storage and computing requirements.

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