On 2/12/2020 4:11 PM, Warner Losh wrote:
On Wed, Feb 12, 2020, 11:13 AM Clem Cole <clemc(a)ccc.com
<mailto:clemc@ccc.com>> wrote:
On Tue, Feb 11, 2020 at 10:01 PM Larry McVoy <lm(a)mcvoy.com
<mailto:lm@mcvoy.com>> wrote:
What little Fortran background I have suggests that the difference
might be mind set. Fortran programmers are formally trained (at
least I
was, there was a whole semester devoted to this) in accumulated
errors.
You did a deep dive into how to code stuff so that the error was
reduced
each time instead of increased. It has a lot to do with how
floating
point works, it's not exact like integers are.
Just a thought, but it might also be the training. My Dad (a
mathematician and 'computer') passed a few years ago, I'd love to
have asked him. But I suspect when he and his peeps were doing
this with a slide rule or at best an Friden mechanical adding
machine, they were acutely aware of how errors accumulated or not.
When they started to convert their processes/techniques to Fortran
in the early 1960s, I agree with you that I think they were
conscious of what they were doing. I'm not sure modern CS types
are taught the same things as what might be taught in a course being
run by a pure scientist who cares in the same way folks like our
mothers and fathers did in the 1950s and 60s.
Most cs types barely know that 2.234 might not be an exact number when
converted to binary... A few, however can do sophisticated analysis on
the average ULP for complex functions over the expected range..
If that is true of some today, that is sad and disappointing. I think I
was taught otherwise in my beginning C.S. course at UT-Austin in 1971.
If I recall correctly:
- all doctoral candidates ended up taking two semesters of numerical
analysis. I still have two volume n.a. text in the attic (orange, but
not "burnt orange", IIRC).
- numerical analysis was covered on the doctoral qualifying exam.
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