In my article “Using Integers to Check a Floating-Point Approximation,” I briefly mentioned “bigcomp,” an optimization strtod() uses to reduce big integer overhead when checking long decimal inputs. bigcomp does a floating-point to decimal conversion — right in the middle of a decimal to floating-point conversion mind you — to generate the decimal expansion of the number halfway between two target floating-point numbers. This decimal expansion is compared to the input decimal string, and the result of the comparison dictates which of the two target numbers is the correctly rounded result.
In this article, I’ll explain how bigcomp works, and when it applies. Also, I’ll talk briefly about its performance; my informal testing shows that, under the default setting, bigcomp actually worsens performance for some inputs.
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In general, to convert an arbitrary decimal number into a binary floating-point number, arbitrary-precision arithmetic is required. However, a subset of decimal numbers can be converted correctly with just ordinary limited-precision IEEE floating-point arithmetic, taking what I call the fast path to conversion. Fast path conversion is an optimization used in practice: it’s in David Gay’s strtod() function and in Java’s FloatingDecimal class. I will explain how fast path conversion works, and describe the set of numbers that qualify for it.
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Producing correctly rounded decimal to floating-point conversions is hard, but only because it is made to be done efficiently. There is a simple algorithm that produces correct conversions, but it’s too slow — it’s based entirely on arbitrary-precision integer arithmetic. Nonetheless, you should know this algorithm, because it will help you understand the highly-optimized conversion routines used in practice, like David Gay’s strtod() function. I will outline the algorithm, which is easily implemented in a language like C, using a “big integer” library like GMP.
Ratio of Big Integers (2119/1020) Producing the 53-Bit Significand of 1e-20
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The decimal representations of oppositely signed powers of two and powers of five look alike, as seen in these examples: 2-3 = 0.125 and 53 = 125; 5-5 = 0.00032 and 25 = 32. The significant digits in each pair of powers is the same, even though one is a fraction and one is an integer. In other words, a negative power of one base looks like a positive power of the other.
Powers of Two and Powers of Five that Look Alike
This relationship is not coincidence; it’s a by-product of how fractions are represented as decimals. I’ll show you simple algebra that proves it, as well as algebra that proves similar properties — in products involving negative powers.
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PARI/GP is an open source computer algebra system I use frequently in my study of binary numbers. It doesn’t manipulate binary numbers directly — input, and most output, is in decimal — so I use it mainly to do the next best thing: calculate with powers of two. Calculations with powers of two are, indirectly, calculations with binary numbers.
PARI/GP is a sophisticated tool, with several components — yet it’s easy to install and use. I use its command shell in particular, the PARI/GP calculator, or gp for short. I will show you how to use simple gp commands to explore binary numbers.
PARI/GP Calculator (Sample of Calculations Used to Explore Binary Numbers)
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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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The following infinite set of numbers is known as the powers of two:
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Why are they called powers of two? What is the pattern you see? How is the set described mathematically? What are the set’s components? We will answer those questions in this article.
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Now that you know how the powers of two are named, lets look at other, nonstandard ways to name them. You will see these names on the internet as well as in books. We will not use them on this site other than in this article, and we only discuss them here to make you aware of their use. As a by-product of this discussion, you may gain some insight into the nature of the powers of two. But beware — you may become confused as well!
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How can you tell if a number is a power of two?
That’s easy if it’s in the form 2n, where n is an integer. For example, 212, 20, and 2-37 are powers of two. That is by definition. But what about arbitrary positive numbers like 16,392, 524,288, or 0.00390625? Are they powers of two? Here’s how to tell — if they can be simplified to the form 2n, they are; if they can’t, they’re not.
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Powers of two can be combined, under the laws of exponents, to create other powers of two. Under these rules, you can multiply powers of two, divide powers of two, or raise a power of two to a power and still get another power of two. You can combine these rules to create complicated expressions, expressions that result in a single power of two. For example,
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The laws of exponents apply generally to any base; two is no different. But since we’re interested in powers of two, we’ll couch them in terms of powers of two. Once we explain the laws in this way, you’ll understand the math behind the example above.
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(Published December 2nd, 2011) 
