In the 1960s, I. Bennett Goldberg and David W. Matula published papers relating floating-point number systems of different bases, showing the conditions under which conversions between them round-trip; that is, when conversion to another base and back returns the original number. Independently, both authors derived the formula that specifies the number of significant digits required for round-trip conversions.

In his paper “27 Bits Are Not Enough for 8-Digit Accuracy”, Goldberg shows the formula in the context of decimal to binary floating-point conversions. He starts with a simple example — a 7-bit binary floating-point system — and shows that it does not have enough precision to round-trip all 2-digit decimal floating-point numbers. I took his example and put it into diagrams, giving you a high level view of what governs round-trip conversions. I also extended his example to show that the same concept applies to binary to decimal floating-point round-trips.

Relative Spacing Governs Round-Trips

The well-known digit counts for round-trip conversions to and from IEEE 754 floating-point are dictated by these same principles.

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In a computer, decimal floating-point numbers are converted to binary floating-point numbers for calculation, and binary floating-point numbers are converted to decimal floating-point numbers for display or storage. In general, these conversions are inexact; they are rounded, and rounding is governed by the spacing of numbers in each set.

Floating-point numbers are unevenly spaced, and the spacing varies with the base of the number system. Binary floating-point numbers have power of two sized gaps that change size at power of two boundaries. Decimal floating-point numbers are similarly spaced, but with power of ten sized gaps changing size at power of ten boundaries. In this article, I will discuss the spacing of decimal floating-point numbers.

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An IEEE 754 binary floating-point number is a number that can be represented in normalized binary scientific notation. This is a number like 1.00000110001001001101111 x 2^{-10}, which has two parts: a *significand*, which contains the significant digits of the number, and a power of two, which places the “floating” radix point. For this example, the power of two turns the shorthand 1.00000110001001001101111 x 2^{-10} into this ‘longhand’ binary representation: 0.000000000100000110001001001101111.

The significands of IEEE binary floating-point numbers have a limited number of bits, called the *precision*; single-precision has 24 bits, and double-precision has 53 bits. The range of power of two exponents is also limited: the exponents in single-precision range from -126 to 127; the exponents in double-precision range from -1022 to 1023. (The example above is a single-precision number.)

Limited precision makes binary floating-point numbers discontinuous; there are *gaps* between them. Precision determines the *number* of gaps, and precision and exponent together determine the *size* of the gaps. Gap size is the same between consecutive powers of two, but is different for every consecutive pair.

Gaps Between Binary Floating-Point Numbers In a Toy Floating-Point System

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In the strictest sense, converting a decimal number to binary floating-point means putting it in IEEE 754 format — a multi-byte structure composed of a sign field, an exponent field, and a significand field. Viewing it in this raw form (binary or hex) is useful, but there are other forms that are more enlightening.

I’ve written an online converter that takes a decimal number as input, converts it to floating-point, and then displays its exact floating-point value in nine forms (including the two raw IEEE forms). I will show examples of these forms in this article.

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While testing my new decimal to floating-point converter I discovered a bug in old territory: PHP incorrectly converts the number 2.2250738585072012e-308.

<?php printf("%.17g",2.2250738585072012e-308); ?>

This prints 2.2250738585072009E-308; it should print 2.2250738585072014e-308. (I verified that the internal double value is wrong; the printed value correctly represents it.)

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On Monday, Psy’s Gangnam Style video exceeded the limit of YouTube’s view counter; this is what Google had to say (hat tip: Digg):

“We never thought a video would be watched in numbers greater than a 32-bit integer (=2,147,483,647 views)…”

2,147,483,647 is 2^{31} – 1, the maximum positive value a 32-bit signed integer can contain.

Google has since fixed the counter, but they didn’t say how (32-bit unsigned integer? 64-bit integer?). (*Update:* By deduction from this Wall Street Journal article, Google is now using 64-bit signed integers — although the number they cite is 2^{63}, not 2^{63} – 1.)

The interesing thing is the “Easter egg” Google placed. If you hover your mouse over the counter, it spins like a slot machine; if you hold the mouse there long enough it will show a negative number. **But the negative number is not what I expected.** Is there a bug in the Easter egg?

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With a little research and some USB tracing, I wrote a Windows program — *and an Android app* — that turns my Fretlight guitar into a BCD mode binary clock!

My Fretlight BCD Clock (**click image for video**)

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To complete my exploration of numbers in App Inventor, I’ve written an app that converts integers between decimal and binary. It uses the standard algorithms, which I’ve just translated into blocks.

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I recently wrote that App Inventor represents its numbers in floating-point. I’ve since discovered something curious about integers. When typed into math blocks, they are represented in floating-point; but when generated through calculations, they are represented as arbitrary-precision integers — *big integers*.

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Variants of the question “Is floating point math broken?” are asked every day on Stackoverflow.com. I don’t think the questions will ever stop, not even by the year 2091 (that’s the year that popped into my head after just reading the gazillionth such question).

A representative floating-point question asked on stackoverflow.com in 2009, projected to be just as popular in 2091 (**click thumbnail to enlarge**)

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After discovering that App Inventor represents numbers in floating-point, I wanted to see how it handled some edge case decimal/floating-point conversions. In one group of tests, I gave it numbers that were converted to floating-point incorrectly in other programming languages (I include the famous PHP and Java numbers). In another group of tests, I gave it numbers that, when converted to floating-point and back, demonstrate the rounding algorithm used when printing halfway cases. It turns out that App Inventor converts all examples correctly, and prints numbers using the round-half-to-even rule.

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I am exploring App Inventor, an Android application development environment for novice programmers. I am teaching it to my kids, as an introductory step towards “real” app development. While playing with it I wondered: are its numbers implemented in decimal? No, they aren’t. They are implemented in double-precision binary floating-point. I put together a few simple block programs to demonstrate this, and to expose the usual floating-point “gotchas”.

App Inventor Blocks To Display 0.1 to 17 Digits

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