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Comment by saagarjha | Hacker News Reader

-ffast-math should rebrand itself to -ffast-and-more-correct-math ;)

It’s normally not more correct.

;)

I thought fast-math meant (among other things) that the compiler could algebraically simplify things using some rules for real numbers that don't apply to 32-/64-bit floating point numbers. I'd expect that reducing the number of operations would more often than not reduce the error due to rounding, which is what most people would want when they talk about "correctness".

Which step in this (very naive) argument is wrong?

The code lacking -ffast-math doing math incorrectly, and -ffast-math ending up doing it correctly by accident.

Stuff like Kahan summation breaks horribly with -ffast-math, because if you assume that sum is associative the error term, which is ((s+x)-s)-x, simplifies to zero.

https://en.m.wikipedia.org/wiki/Kahan_summation_algorithm

Yeah, that's the obvious counterexample, but that's why I said "more often than not". The statement I was questioning was that fast-math is "normally not more correct."

That's where you dust off volatile.

Sadly true. That's the only portable way I found to implement something akind to a Kahan sum so that no compiler / flag would destroy it.

Rounding error is not the only source of error with floating point. There is also loss of significance, which in the worst case is called catastrophic cancellation [1]. This occurs when subtracting two numbers which are very close in magnitude, for example:

1.23456789 - 1.23456788 = 0.00000001 = 1 * 10^-8

So here we’ve gone from 9 significant figures down to 1. This phenomenon will make a naïve Taylor series approximation of e^x be very inaccurate for negative x, due to the sign alternating between positive and negative on every term, causing a lot of catastrophic cancellation.

[1] https://en.wikipedia.org/wiki/Loss_of_significance

(Note that in your example no accuracy is lost)

One very language-lawyer way to look at it is that, even if the answer it gives is more mathematically accurate, it's still less correct in that it's not the value you literally asked for.

Imagine the analogue for integers. You might use code like this to round down to a multiple of 2:

    int i = 7;
    int j = (i / 2) * 2;

If a compiler optimized that to j = i then that's more mathematically accurate but less correct.

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