16.1 A General Description of Computer Arithmetic

Until now, we have worked with data as either numbers or strings. Ultimately, however, computers represent everything in terms of binary digits, or bits. A decimal digit can take on any of 10 values: zero through nine. A binary digit can take on any of two values, zero or one. Using binary, computers (and computer software) can represent and manipulate numerical and character data. In general, the more bits you can use to represent a particular thing, the greater the range of possible values it can take on.

Modern computers support at least two, and often more, ways to do arithmetic. Each kind of arithmetic uses a different representation (organization of the bits) for the numbers. The kinds of arithmetic that interest us are:

Decimal arithmetic

This is the kind of arithmetic you learned in elementary school, using paper and pencil (and/or a calculator). In theory, numbers can have an arbitrary number of digits on either side (or both sides) of the decimal point, and the results of a computation are always exact.

Some modern systems can do decimal arithmetic in hardware, but usually you need a special software library to provide access to these instructions. There are also libraries that do decimal arithmetic entirely in software.

Despite the fact that some users expect gawk to be performing decimal arithmetic,99 it does not do so.

Integer arithmetic

In school, integer values were referred to as “whole” numbers—that is, numbers without any fractional part, such as 1, 42, or −17. The advantage to integer numbers is that they represent values exactly. The disadvantage is that their range is limited.

In computers, integer values come in two flavors: signed and unsigned. Signed values may be negative or positive, whereas unsigned values are always greater than or equal to zero.

In computer systems, integer arithmetic is exact, but the possible range of values is limited. Integer arithmetic is generally faster than floating-point arithmetic.

Floating-point arithmetic

Floating-point numbers represent what were called in school “real” numbers (i.e., those that have a fractional part, such as 3.1415927). The advantage to floating-point numbers is that they can represent a much larger range of values than can integers. The disadvantage is that there are numbers that they cannot represent exactly.

Modern systems support floating-point arithmetic in hardware, with a limited range of values. There are software libraries that allow the use of arbitrary-precision floating-point calculations.

POSIX awk uses double-precision floating-point numbers, which can hold more digits than single-precision floating-point numbers. gawk has facilities for performing arbitrary-precision floating-point arithmetic, which we describe in more detail shortly.

Computers work with integer and floating-point values of different ranges. Integer values are usually either 32 or 64 bits in size. Single-precision floating-point values occupy 32 bits, whereas double-precision floating-point values occupy 64 bits. (Quadruple-precision floating point values also exist. They occupy 128 bits, but such numbers are not available in awk.) Floating-point values are always signed. The possible ranges of values are shown in Table 16.1 and Table 16.2.

RepresentationMinimum valueMaximum value
32-bit signed integer−2,147,483,6482,147,483,647
32-bit unsigned integer04,294,967,295
64-bit signed integer−9,223,372,036,854,775,8089,223,372,036,854,775,807
64-bit unsigned integer018,446,744,073,709,551,615

Table 16.1: Value ranges for integer representations

RepresentationMinimum positive nonzero valueMinimum finite valueMaximum finite value
Single-precision floating-point1.175494*10-38-3.402823*10383.402823*1038
Double-precision floating-point2.225074*10-308-1.797693*103081.797693*10308
Quadruple-precision floating-point3.362103*10-4932-1.189731*1049321.189731*104932

Table 16.2: Approximate value ranges for floating-point number representations



We don’t know why they expect this, but they do.