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Tools that help handle floating-point dragons

There be dragons is a common refrain in any discussion involving code containing floating-point. The dragons are not likely to disappear anytime soon, but there has been a lot of progress since my 2011 post and practical tools for handling them are starting to become available to developers who are not numerical analysts.

All the techniques contain an element of brute force, very many possibilities are examined (cloud computing is starting to have a big impact on how problems are attacked). All the cloud computing on the planet would not make a noticeable dent in any problem unless some clever stuff was done to drastically prune the search space.

My current favorite tool is Herbie, if only because of the blog posts describing some of the techniques used (it’s currently limited to code without loops; if you need loop support check out Rosa).

It’s all very well having the performance of your floating-point code optimized, but who is to say nasty problems are not lurking in unexplored ranges of the underlying formula. Without an Oracle capable of generating the correct answer (whatever that might be; Precimonious has to be provided with training inputs), the analysis can only flag what is considered to be suspicious behavior. Craft attempts to detect cancellation errors, S3FP searches for input values that produce results containing large relative error and Rangelab simply provides bounds on the output values calculated from whatever input it is fed.

Being interested in getting very accurate results is a niche market. Surprisingly inaccurate results are good enough for many people and perhaps we should be using a language designed for this market.

Perhaps the problem of efficiently and accurately printing floating-point numbers might finally have just been solved.

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