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Re: OT : Approximate / fast math libraries ?

Les wrote:

and processor optimizations to achieve faster results.  The business of
subtraction is one cycle per subtraction, where as fmod is multiple
cycles to begin with plus call and return overhead.  If the iterative is
known to be some small number repeated subtraction may be faster.  Only
some practical work with the algorithm will tell you the real results.

The compilers I've used put FMOD instructions in line in the code. I
haven't looked at the cycle counts in a long time, but I have done
hardware performance predictions, and I'm here to tellya, cache
hits or misses VASTLY overwhelm the operation of the machine.

The same is true of Floating point operations vs integers.  When floats
had to be calculated by loops with an integer processor, they were
expensive and integers were faster.  Now with high speed floating point
units, simple float operations are quite fast if done in line.  Ditto
for doubles.  It costs no more to calculate doubles than singles except

Its faster to use floats. I've sped up integer programs by converting
to floats and using the FP engine.


When discussing algorithm timing, only the algorithm being used and its
variants can be discussed for realtime applications.  This is one reason
that benchmarks are basically useless in choosing a processor for real
time applications, unless you are using the bench mark algorithm in your
specific application.

The first thing to hit is always the algorithm. No amount of cleverness,
assembly language, or trickery is going to make a bubble sort any
faster than O(N^2). Using heap sort fixes that.


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