desmond wrote:Also, I vaguely recall (but don't have the full details offhand) something about the floating range when you get high up is that the resolution isn't quite linear like integers - the gaps between numbers on the scale get bigger as you go. Maybe you can explain that to us laymen...
That's not so difficult to see, without even considering radix complements and IEEE 754. it's just a general consequence of the fact that the representation uses a nonlinear function and that you have only so many bits available to represent the function parameters (for a format say as "1.decimals x 10 power exponent" you will have a onlya given number of bits for "decimals" and for "exponent").
For fixed point representation (in the form "number dot decimals"), the "fixed number" of bits for "number" and "decimals" means that you have constant gaps between the real numbers that can be represented and numbers that cannot (and will be approximated, typically to the nearest lower representable number).
Say you allocate 2 bits for the decimals, you can represent only .0, .1, .2 and .3 as decimal parts, so that the real numbers 1.15 and 1.16 will end up both as,say 1.1 - the gap between the numbers you can represent will be a constant .1 . In other words, the mapping between reals and representation - so long there is one - is linear because the mapping function is linear.
With a representation that uses a power 10 function (or whatever nonlinear mapping), you still have the same "constant spacing" for the decimal part, but the multiplier will vary nonlinearly, leading to an nonlinear spacing between the representable numbers.
Say you use 2 bits for decimal part and 2 bits the exponent: you will have 0,1,2 and 3 as possible decimal parts (leading to 1.0, 1.1, 1.2 and 1.3 as left multiplier) and 1, 10, 100 and 1000 as possible right multipliers. Therefore, the numbers you can represent are therefore 1, 1.1, 1.2, 1.3, then 10, 11, 12, 13, then again 100, 110, 120, 130 and finally 1000, 1200, 1300, 1400. So the gaps between numbers that can be represented (or equivalently, the approximation error when you map real numbers) varies nonlinearly (and gets bigger for bigger numbers that you want to represent).
The same will happen with any representation using a nonlinear function, with the "gaps" between numbers varying according to the specific nonlinear function used.