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Arthur T Knackerbracket has found the following story:
Professor Peter Coveney, Director of the UCL Centre[*] for Computational Science and study co-author, said: "Our work shows that the behaviour of the chaotic dynamical systems is richer than any digital computer can capture. Chaos is more commonplace than many people may realise and even for very simple chaotic systems, numbers used by digital computers can lead to errors that are not obvious but can have a big impact. Ultimately, computers can't simulate everything."
The team investigated the impact of using floating-point arithmetic -- a method standardised by the IEEE and used since the 1950s to approximate real numbers on digital computers.
Digital computers use only rational numbers, ones that can be expressed as fractions. Moreover the denominator of these fractions must be a power of two, such as 2, 4, 8, 16, etc. There are infinitely more real numbers that cannot be expressed this way.
In the present work, the scientists used all four billion of these single-precision floating-point numbers that range from plus to minus infinity. The fact that the numbers are not distributed uniformly may also contribute to some of the inaccuracies.
First author, Professor Bruce Boghosian (Tufts University), said: "The four billion single-precision floating-point numbers that digital computers use are spread unevenly, so there are as many such numbers between 0.125 and 0.25, as there are between 0.25 and 0.5, as there are between 0.5 and 1.0. It is amazing that they are able to simulate real-world chaotic events as well as they do. But even so, we are now aware that this simplification does not accurately represent the complexity of chaotic dynamical systems, and this is a problem for such simulations on all current and future digital computers."
The study builds on the work of Edward Lorenz of MIT whose weather simulations using a simple computer model in the 1960s showed that tiny rounding errors in the numbers fed into his computer led to quite different forecasts, which is now known as the 'butterfly effect'.
[*] UCL: University College London
Journal Reference:
Bruce M. Boghosian, Peter V. Coveney, Hongyan Wang. A New Pathology in the Simulation of Chaotic Dynamical Systems on Digital Computers. Advanced Theory and Simulations, 2019; 1900125 DOI: 10.1002/adts.201900125
(Score: 2) by DannyB on Thursday September 26 2019, @06:03PM
For money, I don't want more precision. I just want the right amount of precision, and an exact, not approximate, representation. Something that can exactly represent ten dollars and ten cents. Not approximately, but exactly.
It is amusing how money has been represented by integers since as long as recorded history, without computers. Even sometimes in weird bases, like base 60, etc. Before the computer age.
With computers I don't care if it's BCD or 2's compliment integers, as long as it is an integer. And BCD is really an integer, with an implied decimal point. 2's compliment gives you fast arithmetic on all common processors. Converting to and from decimal base 10 representation is an exact process. Doing four-function arithmetic with integers is also exact. Even division has an exact quotient and remainder.
Funny how money was represented prior to computers.
To transfer files: right-click on file, pick Copy. Unplug mouse, plug mouse into other computer. Right-click, paste.