# My Fretlight Guitar As a Binary Clock

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

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

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.

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*.

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).

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.

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”.

GCC was recently fixed so that its decimal to floating-point conversions are done correctly; it now calls the MPFR function mpfr_strtofr() instead of using its own algorithm. However, GCC still does its conversion in two steps: first it converts to an intermediate precision (160 or 192 bits), and then it rounds that result to a target precision (53 bits for double-precision floating-point). That is double rounding — how does it avoid double rounding errors? It uses round-to-odd rounding on the intermediate result.

I want to contribute to the Hour of Code event happening now during Computer Science Education Week.

I don’t write about computer programming, but I do write extensively about how computers work — in particular, about how they do arithmetic with binary numbers. For your “hour of code” I’d like to introduce you to binary numbers and binary addition. I’ve selected several of my articles for you to read, and I’ve written some exercises you can try on my online calculators.

GCC, the GNU Compiler Collection, recently fixed this eight and a half year old bug: “GCC Bugzilla – Bug 21718: real.c rounding not perfect.” This bug was the cause of incorrect decimal string to binary floating-point conversions. I first wrote about it over three years ago, and then recently in September and October. I also just wrote a detailed description of GCC’s conversion algorithm last month.

This fix, which will be available in version 4.9, scraps the old algorithm and replaces it with a call to MPFR function mpfr_strtofr(). I tested the fix on version 4.8.1, replacing its copy of gcc/real.c with the fixed one. I found no incorrect conversions.

While running some of GCC’s string to double conversion testcases I discovered a bug in David Gay’s strtod(): it converts some very small subnormal numbers incorrectly. Unlike numbers 2^{-1075} or smaller, which should convert to zero under round-to-nearest/ties-to-even rounding, numbers between 2^{-1075} and 2^{-1074} should convert to 2^{-1074}, the smallest number representable in double-precision binary floating-point. strtod() correctly converts the former to 0, but it incorrectly converts the latter to 0 as well.

(**Update 11/25/13**: This bug has been fixed.)

In my article “Floating-Point Questions Are Endless on stackoverflow.com” I showed examples of the many questions asked that demonstrate lack of knowledge of the most basic property of floating-point — that not all decimal values are representable in binary. In response to a reader’s comment on my article I wrote:

It would be interesting to know how it’s taught today (it’s been a very long time since I was taught it). I can’t imagine though that the person teaching it wouldn’t say — within a sentence or two of saying “floating-point” — that it “can’t represent all decimal numbers accurately”.

That prompted me to look through my box of thirty plus year old college (undergraduate) notebooks. I found notebooks for four classes in which I was taught floating-point. The notes from three of those classes confirm what I thought — that we were warned early of the decimal/binary mismatch. But in the first class of the four — the beginner’s class — it’s less clear what we were told. I’ll show you images of the relevant excerpts from my notes. (I notice I had some elements of cursive in my handwriting back then.)

A reader of my blog, Water Qian, reported a bug to me after reading my article “How GLIBC’s strtod() Works”. I recently tested strtod(), which was was fixed to do correct rounding in glibc 2.17; I had found no incorrect conversions.

Water tested the conversion of 2^{-1075} — in retrospect an obvious corner case I should have tried — and found that it converted incorrectly to 0x0.0000000000001p-1022. That’s 2^{-1074}, the smallest double-precision value. It should have converted to 0, under round-to-nearest/ties-to-even rounding.

(**Update 11/13/13**: This bug has been fixed for version 2.19.)