为什么数字运算程序在分成NaN时开始运行得慢得多？

Question

A program repeats some calculation over an array of double s. Then something unfortunate happens and NaN get produced... It starts running much slower after this.

-ffast-math does not change a thing.

Why does it happen with -ffast-math ? Shouldn't it prevent throwing floating-point exceptions and just proceed and churn out NaN s at the same rate as usual numbers?

Simple example:

nan.c

#include <stdio.h>
#include <math.h>

int main() {
    long long int i;
    double a=-1,b=0,c=1;

    for(i=0; i<100000000; ++i) {
        a+=0.001*(b+c)/1000;
        b+=0.001*(a+c)/1000;
        c+=0.001*(a+b)/1000;
        if(i%1000000==0) { fprintf(stdout, "%g\n", a); fflush(stdout); }
        if(i==50000000) b=NAN;
    }
    return 0;
}

running:

$ gcc -ffast-math -O3 nan.c -o nan && ./nan  | ts '%.s'
...
1389025567.070093 2.00392e+33
1389025567.085662 1.48071e+34
1389025567.100250 1.0941e+35
1389025567.115273 8.08439e+35
1389025567.129992 5.9736e+36
1389025568.261108 nan
1389025569.385904 nan
1389025570.515169 nan
1389025571.657104 nan
1389025572.805347 nan

Update : Tried various -O3 , -ffast-math , -msse , -msse3 - no effect. Hovewer when I tried building for 64-bits instead of usual 32-bits, it started to process NaNs as fast as other numbers (in addition to general 50% speedup), even without any optimisation options. Why NaNs are so slow in 32-bit mode with -ffast-math ?

Answer 1

Floating point operations on NaN are exceptional cases and definitely take longer to execute. It's important to remember when vectorizing with SSE because any NaNs that sneak into don't-care columns in the registers can still make your code run much slower.

This page includes some performance measurements of math on NaN which is even worse than I thought!

Answer 2

Your compiler defaults to using x87 (which incurs a stall for processing NaNs) when producing a 32-bit executable. Pass -mfpmath=sse to tell it to use SSE (which can handle NaNs at speed) instead.