When Floats Don’t Behave Like Floats

These two programs — compiled with Microsoft Visual C++ and run on a 32-bit Intel Core Duo processor — demonstrate an anomaly that occurs when using single-precision floating point variables:

Program 1

#include "stdio.h"
int main (void)
{
 float f1 = 0.1f, f2 = 3.0f, f3;

 f3 = f1 * f2;
 if (f3 != f1 * f2)
   printf("Not equal\n");
}

Prints “Not equal”.

Program 2

#include "stdio.h"
int main (void)
{
 float f1 = 0.7f, f2 = 10.0f, f3;
 int i1, i2;

 f3 = f1 * f2;
 i1 = (int)f3;
 i2 = (int)(f1 * f2);
 if (i1 != i2)
   printf("Not equal\n");
}

Prints “Not equal”.

In each case, f3 and f1 * f2 differ. But why? I’ll explain what’s going on.

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Displaying the Raw Fields of a Floating-Point Number

A double-precision floating-point number is represented internally as 64 bits, divided into three fields: a sign field, an exponent field, and a fraction field. You don’t need to know this to use floating-point numbers, but knowing it can help you understand them. This article shows you how to access those fields in C code, and how to print them — in binary or hex.

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Converting Floating-Point Numbers to Binary Strings in C

If you want to print a floating-point number in binary using C code, you can’t use printf() — it has no format specifier for it. That’s why I wrote a program to do it, a program I describe in this article.

(If you’re wondering why you’d want to print a floating-point number in binary, I’ll tell you that too.)

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A Simple C Program That Prints 2,098 Powers of Two

To write a computer program to print the first 1000 nonnegative powers of two, do you think you’d need to use arbitrary precision arithmetic? After all, 21000 is a 302-digit number. How about printing the first 1000 negative powers of two? 2-1000 weighs in at a whopping 1000 decimal places. It turns out all you need is standard double-precision floating-point arithmetic — and the right compiler!

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Print Precision of Floating-Point Integers Varies Too

Recently I showed that programming languages vary in how much precision they allow in printed floating-point fractions. Not only do they vary, but most don’t meet my standard — printing, to full precision, decimal values that have exact floating-point representations. Here I’ll present a similar study for floating-point integers, which had similar results.

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Print Precision of Dyadic Fractions Varies by Language

Interestingly, programming languages vary in how much precision they allow in printed floating-point fractions. You would think they’d all be the same, allowing you to print as many decimal places as you ask for. After all, a floating-point fraction is a dyadic fraction; it has as many decimal places as it has bits in its fractional representation.

Consider the dyadic fraction 5,404,319,552,844,595/253. Its decimal expansion is 0.59999999999999997779553950749686919152736663818359375, and its binary expansion is 0.10011001100110011001100110011001100110011001100110011. Both are 53 digits long. The ideal programming language lets you print all 53 decimal places, because all are meaningful. Unfortunately, many languages won’t let you do that; they typically cap the number of decimal places at between 15 and 17, which for our example might be 0.59999999999999998.

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Ten Ways to Check if an Integer Is a Power Of Two in C

To write a program to check if an integer is a power of two, you could follow two basic strategies: check the number based on its decimal value, or check it based on its binary representation. The former approach is more human-friendly but generally less efficient; the latter approach is more machine-friendly but generally more efficient. We will explore both approaches, comparing ten different but equivalent C functions.

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What Powers of Two Look Like Inside a Computer

A power of two, when expressed as a binary number, is easy to spot: it has one, and only one, 1 bit. For example, 1000, 10, and 0.001 are powers of two. Inside a computer, however, numbers are more generally represented in binary code, not as “pure” binary numbers. As a result, you may not be able to look at the binary representation of a number and tell at a glance whether it’s a power of two or not; it depends on how it’s encoded.

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