Execution time bottleneck: How

[Christopher Neufeld]

In article <21658 at yunexus.YorkU.CA> racine at yunexus.yorku.ca (Jeff Racine) writes:
>   for (j=1; j <= n; j++) {
>      sum = 0.0;
>      for (i=1; i<= n; i++) {
>         sum += exp( con * (x[i]-x[j]) * (x[i]-x[j]) );
>      }
>      a[j] = k*sum;
>   }
>This part of the algorithmn is a bottleneck. I want to make this as
>quick as possible. What are my options? I have heard that assembler
>would be faster. How much faster would assembler be? Would it be worth
>the effort? Are there other alternatives that you can see?
>
   Well, this might look obvious, but do those delta-x form a discrete
set? If so, you can use a lookup table and calculate all the
exponentials out in front. It depends on your application, but when I
was doing Monte-Carlo simulations which passed through an evaluation
step like this some 10^6 times per data point, I sped things up by
making a lookup table of some thousands of entries, all worked out ahead
of time, and then modified the algorithm so that it believed that it was
working with integers (to simplify the algebra in calculating the table
index number). If you've got enough memory, and that inner loop is
executed often enough, then an approach like this might help.


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