Optimizations enabled by -ffast-math

This blog post describes the optimizations enabled by -ffast-math when compiling C or C++ code with GCC 11 for x86_64 Linux (other languages/operating systems/CPU architectures may enable slightly different optimizations).


Most of the “fast math” optimizations can be enabled/disabled individually, and -ffast-math enables all of them:1

  • -ffinite-math-only
  • -fno-signed-zeros
  • -fno-trapping-math
  • -fassociative-math
  • -fno-math-errno
  • -freciprocal-math
  • -funsafe-math-optimizations
  • -fcx-limited-range

When compiling standard C (that is, when using -std=c99 etc. instead of the default “GNU C” dialect), -ffast-math also enables -ffp-contract=fast, allowing the compiler to combine multiplication and addition instructions with an FMA instruction (godbolt). C++ and GNU C are not affected as -ffp-contract=fast is already the default for them.

Linking using gcc with -ffast-math does one additional thing – it makes the CPU treat subnormal numbers as 0.0.

Treating subnormal numbers as 0.0

The floating-point format has a special representation for values that are close to 0.0. These “subnormal” numbers (also called “denormals”) are very costly2 in some cases because the CPU handles subnormal results using microcode exceptions.

The x86_64 CPU has a feature to treat subnormal input as 0.0 and flush subnormal results to 0.0, eliminating this performance penalty. This can be enabled by

#define MXCSR_DAZ (1<<6)    /* Enable denormals are zero mode */
#define MXCSR_FTZ (1<<15)   /* Enable flush to zero mode */

unsigned int mxcsr = __builtin_ia32_stmxcsr();

Linking using gcc with -ffast-math makes it disable subnormal numbers for the application by adding this code in a global constructor that runs before main.

-ffinite-math-only and -fno-signed-zeros

Many optimizations are prevented by properties of the floating-point values NaN, Inf, and -0.0. For example:

  • x+0.0 cannot be optimized to x because that is not true when x is -0.0.
  • x-x cannot be optimized to 0.0 because that is not true when x is NaN or Inf.
  • x*0.0 cannot be optimized to 0.0 because that is not true when x is NaN or Inf.
  • The compiler cannot transform
    if (x > y) {
    } else {

    to the form

    if (x <= y) {
    } else {

    (which is a useful optimization when simplifying control flow and ensuring that the common case is handled without taking branches) because that is not true when x or y is NaN.

-ffinite-math-only and -fno-signed-zeros tell the compiler that no calculation will produce NaN, Inf, or -0.0, so the compiler can then do the kind of optimization described above.

Note: The program may behave in strange ways (such as not evaluating either the true or false part of an if-statement) if calculations produce Inf, NaN, or -0.0 when these flags are used.


It is possible to enable trapping of floating-point exceptions by using the GNU libc function feenableexcept to generate the signal SIGFPE when any floating-point instruction overflow, underflow, generates NaN, etc. For example, the function compute below does some calculations that overflow to Inf, which makes the program terminate with SIGFPE when FE_OVERFLOW is enabled.

// Compile as "gcc example.c -D_GNU_SOURCE -O2 -lm"
#include <stdio.h>
#include <fenv.h>

void compute(void) {
  float f = 2.0;
  for (int i = 0; i < 7; ++i) {
    f = f * f;
    printf("%d: f = %f\n", i, f);

int main(void) {

  printf("\nWith overflow exceptions:\n");

  return 0;

This means that the compiler cannot schedule floating-point instructions to execute speculatively, as the speculated instruction could then generate a signal for cases where the original program did not. For example, the calculation x/y is constant in the loop below, but the compiler cannot hoist it out of the loop because that could cause the function to execute x/y for cases where all elements of arr are larger than 0.0 and could therefore crash for cases where the original program did not (godbolt).

double arr[1024];

void foo(int n, double x, double y) {
  for (int i = 0; i < n; ++i) {
    if (arr[i] > 0.0)
      arr[i] = x / y;

One other fun special case is C floating-point atomics. The C standard requires that floating-point exceptions are discarded when doing compound assignment (see “compound assignment” in the C standard), so the compiler must insert extra code unless trapping-math is disabled (godbolt).

Passing -fno-trapping-math tells the compiler that the program will not enable floating-point exceptions, and the compiler can then do these optimizations.


-fassociative-math allows re-association of operands in series of floating-point operations (as well as a few more general reordering optimizations). Most of the optimizations need -fno-trapping-math, -ffinite-math-only, and -fno-signed-zeros too.

Some examples of -fassociative-math optimizations:

Original Optimized
(X + Y) - X Y
(X * Z) + (Y * Z) (X + Y) * Z
(X * C) + X X * (C + 1.0)     when C is a constant
(C1 / X) * C2 (C1 * C2) / X     when C1 and C2 are constants
(C1 - X) < C2 (C1 - C2) > X     when C1 and C2 are constants

Re-association is especially useful for vectorization. Consider, for example, the loop (godbolt)

float a[1024];

float foo(void) {
  float sum = 0.0f;
  for (int i = 0; i < 1024; ++i) {
    sum += a[i];
  return sum;

All additions to sum are made serially, so the calculations cannot normally be vectorized. But -fassociative-math permits the compiler to change the order to

sum = (a[0] + a[4] + ... + a[1020]) + (a[1] + a[5] + ... + a[1021]) + (a[2] + a[6] + ... + a[1022]) + (a[3] + a[7] + ... + a[1023]);

and can compile the loop as if it was written as

float a[1024];

float foo(void) {
  float sum0 = sum1 = sum2 = sum3 = 0.0f;
  for (int i = 0; i < 1024; i += 4) {
    sum0 += a[i    ];
    sum1 += a[i + 1];
    sum2 += a[i + 2];
    sum3 += a[i + 3];
  return sum0 + sum1 + sum2 + sum3;

which is easy to vectorize.


The C mathematical functions may set errno if called with invalid input. This possible side effect means that the compiler must call the libc function instead of using instructions that can calculate the result directly.

The compiler can, in some cases, mitigate the problem by not calling the function when it knows the operation will succeed. For example, (godbolt)

double foo(double x) {
  return sqrt(x);

is compiled to code using the sqrtsd instruction when the input is in range and only calls sqrt for input that will return NaN

        pxor    xmm1, xmm1
        ucomisd xmm1, xmm0
        ja      .L10
        sqrtsd  xmm0, xmm0
        jmp     sqrt

This eliminates most of the overhead (as the comparison/branch is essentially free when predicted correctly), but this extra branch and function call makes life harder for other optimizations (such as vectorization).

-fno-math-errno makes GCC optimize all math functions as if they do not set errno (that is, the compiler does not need to call the libc functions if the architecture has suitable instructions).

Non-math functions

One surprising effect of -fno-math-errno is that it makes GCC believe that memory-allocating libc functions (such as malloc and strdup) do not set errno. This can be seen in the function below where -fno-math-errno makes GCC optimize away the call to perror (godbolt)

void *foo(size_t size) {
  errno = 0;
  void *p = malloc(size);
  if (p == NULL) {
    if (errno)
  return p;

This is reported as GCC bug 88576.


-freciprocal-math allows the compiler to compute x/y as x*(1/y). This is useful for code of the form (godbolt)

float length = sqrtf(x*x + y*y + z*z);
x = x / length;
y = y / length;
z = z / length;

where the compiler now can generate the code as if it was written as

float t = 1.0f / sqrtf(x*x + y*y + z*z);
x = x * t;
y = y * t;
z = z * t;

This optimization generates more instructions, but the resulting code is, in general, better as multiplication is faster than division and can execute on more ports in the CPU.


-funsafe-math-optimizations enables various “mathematically correct” optimizations that may change the result because of how floating-point numbers work. Some examples of this are

Original Optimized
sqrt(x)*sqrt(x) x
sqrt(x)*sqrt(y) sqrt(x*y)
exp(x)*exp(y) exp(x+y)
x/exp(y) x*exp(-y)
x*pow(x,c) pow(x,c+1)
pow(x,0.5) sqrt(x)
(int)log(d) ilog(d)
sin(x)/cos(x) tan(x)

Note: Many of these optimizations need additional flags, such as -ffinite-math-only and -fno-math-errno, in order to trigger.

-funsafe-math-optimizations also enables

  • -fno-signed-zeros
  • -fno-trapping-math
  • -fassociative-math
  • -freciprocal-math

as well as -ffp-contract=fast (for standard C) and treating subnormal numbers as 0.0 in the same way as described for -ffast-math.


The mathematical formulas for multiplying and dividing complex numbers are \[(a + ib) \times (c + id) = (ac - bd) + i(bc + ad)\] \[\frac{a + ib}{c + id} = \frac{ac + bd}{c^2 + d^2} + i\frac{bc - ad}{c^2 + d^2} \] but these does not work well for floating-point values.

One problem is that the calculations may turn overflowed values into NaN instead of Inf, so the implementation needs to adjust. Multiplication ends up with code similar to

double complex
mul(double a, double b, double c, double d) {
  double ac, bd, ad, bc, x, y;
  double complex res;

  ac = a * c;
  bd = b * d;
  ad = a * d;
  bc = b * c;

  x = ac - bd;
  y = ad + bc;

  if (isnan(x) && isnan(y)) {
    /* Recover infinities that computed as NaN + iNaN.  */
    _Bool recalc = 0;
    if (isinf(a) || isinf(b)) {
      /* z is infinite.  "Box" the infinity and change NaNs
       * in the other factor to 0.  */
      a = copysign(isinf(a) ? 1.0 : 0.0, a);
      b = copysign(isinf(b) ? 1.0 : 0.0, b);
      if (isnan(c)) c = copysign(0.0, c);
      if (isnan(d)) d = copysign(0.0, d);
      recalc = 1;
    if (isinf(c) || isinf(d)) {
      /* w is infinite.  "Box" the infinity and change NaNs
       * in the other factor to 0.  */
      c = copysign(isinf(c) ? 1.0 : 0.0, c);
      d = copysign(isinf(d) ? 1.0 : 0.0, d);
      if (isnan(a)) a = copysign(0.0, a);
      if (isnan(b)) b = copysign(0.0, b);
      recalc = 1;
    if (!recalc
        && (isinf(ac) || isinf(bd)
            || isinf(ad) || isinf(bc))) {
      /* Recover infinities from overflow by changing NaNs
       * to 0.  */
      if (isnan(a)) a = copysign(0.0, a);
      if (isnan(b)) b = copysign(0.0, b);
      if (isnan(c)) c = copysign(0.0, c);
      if (isnan(d)) d = copysign(0.0, d);
      recalc = 1;
    if (recalc) {
      x = INFINITY * (a * c - b * d);
      y = INFINITY * (a * d + b * c);

  __real__ res = x;
  __imag__ res = y;
  return res;

One other problem is that the calculations can overflow even when the result of the operation is in range. This is especially problematic for division, so the implementation of division needs to add even more extra code (see the C standard for more details).

-fcx-limited_range makes the compiler use the usual mathematical formulas for complex multiplication/division.

  1. It also enables -fno-signaling-nans, -fno-rounding-math, and -fexcess-precision=fast, which are enabled by default when compiling C or C++ code for x86_64 Linux, so I will not describe them in this blog post. 

  2. For example, Agner Fog’s microarchitecture document says that Broadwell has a penalty of approximately 124 clock cycles when an operation on normal numbers gives a subnormal result. 

Written on October 19, 2021