Comments by Alex_GR (Top 8 by date)

In fact what the program do is to perform calculus over big data series, I assume that this operations was acumulating small errors that at the end cause the problem. Now I have solved this, I have correct results, my doubt is what is the better way to achieve this results, disabling some optimizations for the critical methods (that is what I have already done) or use fp:strict mode in my program (in that mode optimizations that affect my code is also disabled).

What I am going to do is to write a test program that performs floating point operations that is said to be optimized under fp:precise and not under fp:strict and then measure performance, is not what I was looking for but is better than nothing. From stuff I have already read probably there is a little difference in performance between precise and strict model. I will post here my results. Thanks to everybody.

First of all thank you for your answer.
I´m sorry but probably I didn´t explain my problem correctly.
As you said, Debug and release performance aren´t comparable. And I don´t want to compare this.
On the other hand, I had differents results between debug and release versions. Now I had solved this, the differences were due to some floating point optimizations, to be more specific, some that affect FPU flags and mode. I disable those optimizations using the fenv_access directive. Using fp:strict model those optimizations are disabled too.

My doubt is because probably is better to use fp:strict as floating point model for my program. So What I am looking for is some kind of analysis or general overview of the performance issues of the fp:strict model and its differences with fp:precise.