9 Jan
2019
9 Jan
'19
9:43 a.m.
On 01/09/2019 07:49 AM, Paul Koning via cctalk wrote:
Understanding rounding errors is perhaps the most
significant part of "numerical methods", a subdivision of
computer science not as widely known as it should be. I
remember learning of the work of a scientist at DEC whose
work was all about this: making the DEC math libraries not
only efficient but accurate to the last bit. Apparently
this isn't anywhere near as common as it should be. And I
wonder how many computer models are used for answering
important questions where the answers are significantly
affected by numerical errors. Do the authors of those
models know about these considerations? Maybe. Do the
users of those models know? Probably not. paul
A real problem on the IBM 360 and
370 was their floating
point scheme. They saved exponent bits by making the
exponent a power of 16, instead of 2. This meant that the
result of any calculation could end up normalized with up to
3 most-significant zeros. That would reduce the precision
of the number by up to 3 bits, or a factor of 8. Some
iterative solutions compared small differences in successive
calculations to decide when they had converged sufficiently
to stop.
These could either stop early, or run on for a long time
trying to reach convergence.
IBM eventually had to offer IEEE floating point format on
later machines.
Jon