Kahan's exp quotient

Percentage Accurate: 53.3% → 100.0%

Time: 3.7s

Alternatives: 11

Speedup: 8.8×

• 25.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \frac{e^{x} - 1}{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (- (exp x) 1.0) x))

C

double code(double x) {
	return (exp(x) - 1.0) / x;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x)
use fmin_fmax_functions
    real(8), intent (in) :: x
    code = (exp(x) - 1.0d0) / x
end function

Java

public static double code(double x) {
	return (Math.exp(x) - 1.0) / x;
}

Python

def code(x):
	return (math.exp(x) - 1.0) / x

Julia

function code(x)
	return Float64(Float64(exp(x) - 1.0) / x)
end

MATLAB

function tmp = code(x)
	tmp = (exp(x) - 1.0) / x;
end

Wolfram

code[x_] := N[(N[(N[Exp[x], $MachinePrecision] - 1.0), $MachinePrecision] / x), $MachinePrecision]

Initial Program: 53.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{e^{x} - 1}{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (- (exp x) 1.0) x))

C

double code(double x) {
	return (exp(x) - 1.0) / x;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x)
use fmin_fmax_functions
    real(8), intent (in) :: x
    code = (exp(x) - 1.0d0) / x
end function

Java

public static double code(double x) {
	return (Math.exp(x) - 1.0) / x;
}

Python

def code(x):
	return (math.exp(x) - 1.0) / x

Julia

function code(x)
	return Float64(Float64(exp(x) - 1.0) / x)
end

MATLAB

function tmp = code(x)
	tmp = (exp(x) - 1.0) / x;
end

Wolfram

code[x_] := N[(N[(N[Exp[x], $MachinePrecision] - 1.0), $MachinePrecision] / x), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\mathsf{expm1}\left(x\right)}{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (expm1 x) x))

C

double code(double x) {
	return expm1(x) / x;
}

Java

public static double code(double x) {
	return Math.expm1(x) / x;
}

Python

def code(x):
	return math.expm1(x) / x

Julia

function code(x)
	return Float64(expm1(x) / x)
end

Wolfram

code[x_] := N[(N[(Exp[x] - 1), $MachinePrecision] / x), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\frac{e^{x} - 1}{x} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift--.f64 N/A

      \[\leadsto \frac{\color{blue}{e^{x} - 1}}{x} \]

   2. lift-exp.f64 N/A

      \[\leadsto \frac{\color{blue}{e^{x}} - 1}{x} \]

   3. lower-expm1.f64 100.0

      \[\leadsto \frac{\color{blue}{\mathsf{expm1}\left(x\right)}}{x} \]

4. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\frac{\mathsf{expm1}\left(x\right)}{x}} \]
5. Add Preprocessing

Alternative 2: 69.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{e^{x} - 1}{x} \leq 2:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(\left(x \cdot x\right) \cdot \mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right)\right) \cdot x}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (/ (- (exp x) 1.0) x) 2.0)
   1.0
   (/ (* (* (* x x) (fma 0.041666666666666664 x 0.16666666666666666)) x) x)))

C

double code(double x) {
	double tmp;
	if (((exp(x) - 1.0) / x) <= 2.0) {
		tmp = 1.0;
	} else {
		tmp = (((x * x) * fma(0.041666666666666664, x, 0.16666666666666666)) * x) / x;
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (Float64(Float64(exp(x) - 1.0) / x) <= 2.0)
		tmp = 1.0;
	else
		tmp = Float64(Float64(Float64(Float64(x * x) * fma(0.041666666666666664, x, 0.16666666666666666)) * x) / x);
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[N[(N[(N[Exp[x], $MachinePrecision] - 1.0), $MachinePrecision] / x), $MachinePrecision], 2.0], 1.0, N[(N[(N[(N[(x * x), $MachinePrecision] * N[(0.041666666666666664 * x + 0.16666666666666666), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision] / x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 (exp.f64 x) #s(literal 1 binary64)) x) < 2

   1. Initial program 36.4%

      \[\frac{e^{x} - 1}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1} \]
   4. Step-by-step derivation

   5. Applied rewrites 67.8%

      \[\leadsto \color{blue}{1} \]

   if 2 < (/.f64 (-.f64 (exp.f64 x) #s(literal 1 binary64)) x)

   1. Initial program 100.0%

      \[\frac{e^{x} - 1}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{\color{blue}{x \cdot \left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right)}}{x} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right) \cdot \color{blue}{x}}{x} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right) \cdot \color{blue}{x}}{x} \]

      3. +-commutative N/A

         \[\leadsto \frac{\left(x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right) + 1\right) \cdot x}{x} \]

      4. *-commutative N/A

         \[\leadsto \frac{\left(\left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right) \cdot x + 1\right) \cdot x}{x} \]

      5. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right), x, 1\right) \cdot x}{x} \]

      6. +-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right) + \frac{1}{2}, x, 1\right) \cdot x}{x} \]

      7. *-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\left(\frac{1}{6} + \frac{1}{24} \cdot x\right) \cdot x + \frac{1}{2}, x, 1\right) \cdot x}{x} \]

      8. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{6} + \frac{1}{24} \cdot x, x, \frac{1}{2}\right), x, 1\right) \cdot x}{x} \]

      9. +-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{24} \cdot x + \frac{1}{6}, x, \frac{1}{2}\right), x, 1\right) \cdot x}{x} \]

      10. lower-fma.f64 78.8

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right) \cdot x}{x} \]

   5. Applied rewrites 78.8%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right) \cdot x}}{x} \]

   6. Taylor expanded in x around inf

      \[\leadsto \frac{\left({x}^{3} \cdot \left(\frac{1}{24} + \frac{1}{6} \cdot \frac{1}{x}\right)\right) \cdot x}{x} \]
   7. Step-by-step derivation

      1. unpow3 N/A

         \[\leadsto \frac{\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\frac{1}{24} + \frac{1}{6} \cdot \frac{1}{x}\right)\right) \cdot x}{x} \]

      2. pow2 N/A

         \[\leadsto \frac{\left(\left({x}^{2} \cdot x\right) \cdot \left(\frac{1}{24} + \frac{1}{6} \cdot \frac{1}{x}\right)\right) \cdot x}{x} \]

      3. associate-*l* N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(x \cdot \left(\frac{1}{24} + \frac{1}{6} \cdot \frac{1}{x}\right)\right)\right) \cdot x}{x} \]

      4. distribute-lft-in N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(x \cdot \frac{1}{24} + x \cdot \left(\frac{1}{6} \cdot \frac{1}{x}\right)\right)\right) \cdot x}{x} \]

      5. *-commutative N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(\frac{1}{24} \cdot x + x \cdot \left(\frac{1}{6} \cdot \frac{1}{x}\right)\right)\right) \cdot x}{x} \]

      6. *-commutative N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(\frac{1}{24} \cdot x + x \cdot \left(\frac{1}{x} \cdot \frac{1}{6}\right)\right)\right) \cdot x}{x} \]

      7. associate-*r* N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(\frac{1}{24} \cdot x + \left(x \cdot \frac{1}{x}\right) \cdot \frac{1}{6}\right)\right) \cdot x}{x} \]

      8. rgt-mult-inverse N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(\frac{1}{24} \cdot x + 1 \cdot \frac{1}{6}\right)\right) \cdot x}{x} \]

      9. metadata-eval N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(\frac{1}{24} \cdot x + \frac{1}{6}\right)\right) \cdot x}{x} \]

      10. lower-*.f64 N/A

          \[\leadsto \frac{\left({x}^{2} \cdot \left(\frac{1}{24} \cdot x + \frac{1}{6}\right)\right) \cdot x}{x} \]

      11. pow2 N/A

          \[\leadsto \frac{\left(\left(x \cdot x\right) \cdot \left(\frac{1}{24} \cdot x + \frac{1}{6}\right)\right) \cdot x}{x} \]

      12. lift-*.f64 N/A

          \[\leadsto \frac{\left(\left(x \cdot x\right) \cdot \left(\frac{1}{24} \cdot x + \frac{1}{6}\right)\right) \cdot x}{x} \]

      13. lift-fma.f64 78.8

          \[\leadsto \frac{\left(\left(x \cdot x\right) \cdot \mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right)\right) \cdot x}{x} \]

   8. Applied rewrites 78.8%

      \[\leadsto \frac{\left(\left(x \cdot x\right) \cdot \mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right)\right) \cdot x}{x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 63.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{e^{x} - 1}{x} \leq 2:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\left(x \cdot x\right) \cdot 0.16666666666666666\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (/ (- (exp x) 1.0) x) 2.0) 1.0 (* (* x x) 0.16666666666666666)))

C

double code(double x) {
	double tmp;
	if (((exp(x) - 1.0) / x) <= 2.0) {
		tmp = 1.0;
	} else {
		tmp = (x * x) * 0.16666666666666666;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8) :: tmp
    if (((exp(x) - 1.0d0) / x) <= 2.0d0) then
        tmp = 1.0d0
    else
        tmp = (x * x) * 0.16666666666666666d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (((Math.exp(x) - 1.0) / x) <= 2.0) {
		tmp = 1.0;
	} else {
		tmp = (x * x) * 0.16666666666666666;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if ((math.exp(x) - 1.0) / x) <= 2.0:
		tmp = 1.0
	else:
		tmp = (x * x) * 0.16666666666666666
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (Float64(Float64(exp(x) - 1.0) / x) <= 2.0)
		tmp = 1.0;
	else
		tmp = Float64(Float64(x * x) * 0.16666666666666666);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (((exp(x) - 1.0) / x) <= 2.0)
		tmp = 1.0;
	else
		tmp = (x * x) * 0.16666666666666666;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[N[(N[(N[Exp[x], $MachinePrecision] - 1.0), $MachinePrecision] / x), $MachinePrecision], 2.0], 1.0, N[(N[(x * x), $MachinePrecision] * 0.16666666666666666), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 (exp.f64 x) #s(literal 1 binary64)) x) < 2

   1. Initial program 36.4%

      \[\frac{e^{x} - 1}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1} \]
   4. Step-by-step derivation

   5. Applied rewrites 67.8%

      \[\leadsto \color{blue}{1} \]

   if 2 < (/.f64 (-.f64 (exp.f64 x) #s(literal 1 binary64)) x)

   1. Initial program 100.0%

      \[\frac{e^{x} - 1}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1 + x \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot x\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot x\right) + \color{blue}{1} \]

      2. *-commutative N/A

         \[\leadsto \left(\frac{1}{2} + \frac{1}{6} \cdot x\right) \cdot x + 1 \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{1}{2} + \frac{1}{6} \cdot x, \color{blue}{x}, 1\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(\frac{1}{6} \cdot x + \frac{1}{2}, x, 1\right) \]

      5. lower-fma.f64 40.1

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, x, 0.5\right), x, 1\right) \]

   5. Applied rewrites 40.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, x, 0.5\right), x, 1\right)} \]

   6. Taylor expanded in x around inf

      \[\leadsto \frac{1}{6} \cdot \color{blue}{{x}^{2}} \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto {x}^{2} \cdot \frac{1}{6} \]

      2. lower-*.f64 N/A

         \[\leadsto {x}^{2} \cdot \frac{1}{6} \]

      3. unpow2 N/A

         \[\leadsto \left(x \cdot x\right) \cdot \frac{1}{6} \]

      4. lower-*.f64 41.4

         \[\leadsto \left(x \cdot x\right) \cdot 0.16666666666666666 \]

   8. Applied rewrites 41.4%

      \[\leadsto \left(x \cdot x\right) \cdot \color{blue}{0.16666666666666666} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 63.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{e^{x} - 1}{x} \leq 2:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\left(0.16666666666666666 \cdot x\right) \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (/ (- (exp x) 1.0) x) 2.0) 1.0 (* (* 0.16666666666666666 x) x)))

C

double code(double x) {
	double tmp;
	if (((exp(x) - 1.0) / x) <= 2.0) {
		tmp = 1.0;
	} else {
		tmp = (0.16666666666666666 * x) * x;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8) :: tmp
    if (((exp(x) - 1.0d0) / x) <= 2.0d0) then
        tmp = 1.0d0
    else
        tmp = (0.16666666666666666d0 * x) * x
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (((Math.exp(x) - 1.0) / x) <= 2.0) {
		tmp = 1.0;
	} else {
		tmp = (0.16666666666666666 * x) * x;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if ((math.exp(x) - 1.0) / x) <= 2.0:
		tmp = 1.0
	else:
		tmp = (0.16666666666666666 * x) * x
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (Float64(Float64(exp(x) - 1.0) / x) <= 2.0)
		tmp = 1.0;
	else
		tmp = Float64(Float64(0.16666666666666666 * x) * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (((exp(x) - 1.0) / x) <= 2.0)
		tmp = 1.0;
	else
		tmp = (0.16666666666666666 * x) * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[N[(N[(N[Exp[x], $MachinePrecision] - 1.0), $MachinePrecision] / x), $MachinePrecision], 2.0], 1.0, N[(N[(0.16666666666666666 * x), $MachinePrecision] * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (-.f64 (exp.f64 x) #s(literal 1 binary64)) x) < 2

   1. Initial program 36.4%

      \[\frac{e^{x} - 1}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1} \]
   4. Step-by-step derivation

   5. Applied rewrites 67.8%

      \[\leadsto \color{blue}{1} \]

   if 2 < (/.f64 (-.f64 (exp.f64 x) #s(literal 1 binary64)) x)

   1. Initial program 100.0%

      \[\frac{e^{x} - 1}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1 + x \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot x\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot x\right) + \color{blue}{1} \]

      2. *-commutative N/A

         \[\leadsto \left(\frac{1}{2} + \frac{1}{6} \cdot x\right) \cdot x + 1 \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{1}{2} + \frac{1}{6} \cdot x, \color{blue}{x}, 1\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(\frac{1}{6} \cdot x + \frac{1}{2}, x, 1\right) \]

      5. lower-fma.f64 40.1

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, x, 0.5\right), x, 1\right) \]

   5. Applied rewrites 40.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, x, 0.5\right), x, 1\right)} \]

   6. Taylor expanded in x around inf

      \[\leadsto \frac{1}{6} \cdot \color{blue}{{x}^{2}} \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto {x}^{2} \cdot \frac{1}{6} \]

      2. lower-*.f64 N/A

         \[\leadsto {x}^{2} \cdot \frac{1}{6} \]

      3. unpow2 N/A

         \[\leadsto \left(x \cdot x\right) \cdot \frac{1}{6} \]

      4. lower-*.f64 41.4

         \[\leadsto \left(x \cdot x\right) \cdot 0.16666666666666666 \]

   8. Applied rewrites 41.4%

      \[\leadsto \left(x \cdot x\right) \cdot \color{blue}{0.16666666666666666} \]
   9. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(x \cdot x\right) \cdot \frac{1}{6} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(x \cdot x\right) \cdot \frac{1}{6} \]

      3. associate-*l* N/A

         \[\leadsto x \cdot \left(x \cdot \color{blue}{\frac{1}{6}}\right) \]

      4. *-commutative N/A

         \[\leadsto x \cdot \left(\frac{1}{6} \cdot x\right) \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{1}{6} \cdot x\right) \cdot x \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{6} \cdot x\right) \cdot x \]

      7. lower-*.f64 40.1

         \[\leadsto \left(0.16666666666666666 \cdot x\right) \cdot x \]

   10. Applied rewrites 40.1%

       \[\leadsto \left(0.16666666666666666 \cdot x\right) \cdot x \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 73.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x \cdot x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right)\\ \mathbf{if}\;x \leq 2 \cdot 10^{-118}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2.5 \cdot 10^{+77}:\\ \;\;\;\;\frac{\frac{x \cdot x - t\_0 \cdot t\_0}{x - t\_0}}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(\left(x \cdot x\right) \cdot \mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right)\right) \cdot x}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0
         (*
          (* x x)
          (fma (fma 0.041666666666666664 x 0.16666666666666666) x 0.5))))
   (if (<= x 2e-118)
     1.0
     (if (<= x 2.5e+77)
       (/ (/ (- (* x x) (* t_0 t_0)) (- x t_0)) x)
       (/
        (* (* (* x x) (fma 0.041666666666666664 x 0.16666666666666666)) x)
        x)))))

C

double code(double x) {
	double t_0 = (x * x) * fma(fma(0.041666666666666664, x, 0.16666666666666666), x, 0.5);
	double tmp;
	if (x <= 2e-118) {
		tmp = 1.0;
	} else if (x <= 2.5e+77) {
		tmp = (((x * x) - (t_0 * t_0)) / (x - t_0)) / x;
	} else {
		tmp = (((x * x) * fma(0.041666666666666664, x, 0.16666666666666666)) * x) / x;
	}
	return tmp;
}

Julia

function code(x)
	t_0 = Float64(Float64(x * x) * fma(fma(0.041666666666666664, x, 0.16666666666666666), x, 0.5))
	tmp = 0.0
	if (x <= 2e-118)
		tmp = 1.0;
	elseif (x <= 2.5e+77)
		tmp = Float64(Float64(Float64(Float64(x * x) - Float64(t_0 * t_0)) / Float64(x - t_0)) / x);
	else
		tmp = Float64(Float64(Float64(Float64(x * x) * fma(0.041666666666666664, x, 0.16666666666666666)) * x) / x);
	end
	return tmp
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[(x * x), $MachinePrecision] * N[(N[(0.041666666666666664 * x + 0.16666666666666666), $MachinePrecision] * x + 0.5), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, 2e-118], 1.0, If[LessEqual[x, 2.5e+77], N[(N[(N[(N[(x * x), $MachinePrecision] - N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision] / N[(x - t$95$0), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision], N[(N[(N[(N[(x * x), $MachinePrecision] * N[(0.041666666666666664 * x + 0.16666666666666666), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision] / x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < 1.99999999999999997e-118

   1. Initial program 39.7%

      \[\frac{e^{x} - 1}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1} \]
   4. Step-by-step derivation

   5. Applied rewrites 64.4%

      \[\leadsto \color{blue}{1} \]

   if 1.99999999999999997e-118 < x < 2.50000000000000002e77

   1. Initial program 45.4%

      \[\frac{e^{x} - 1}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{\color{blue}{x \cdot \left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right)}}{x} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right) \cdot \color{blue}{x}}{x} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right) \cdot \color{blue}{x}}{x} \]

      3. +-commutative N/A

         \[\leadsto \frac{\left(x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right) + 1\right) \cdot x}{x} \]

      4. *-commutative N/A

         \[\leadsto \frac{\left(\left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right) \cdot x + 1\right) \cdot x}{x} \]

      5. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right), x, 1\right) \cdot x}{x} \]

      6. +-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right) + \frac{1}{2}, x, 1\right) \cdot x}{x} \]

      7. *-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\left(\frac{1}{6} + \frac{1}{24} \cdot x\right) \cdot x + \frac{1}{2}, x, 1\right) \cdot x}{x} \]

      8. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{6} + \frac{1}{24} \cdot x, x, \frac{1}{2}\right), x, 1\right) \cdot x}{x} \]

      9. +-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{24} \cdot x + \frac{1}{6}, x, \frac{1}{2}\right), x, 1\right) \cdot x}{x} \]

      10. lower-fma.f64 62.1

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right) \cdot x}{x} \]

   5. Applied rewrites 62.1%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right) \cdot x}}{x} \]

   7. Applied rewrites 88.5%

      \[\leadsto \frac{\frac{x \cdot x - \left(\left(x \cdot x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right)\right) \cdot \left(\left(x \cdot x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right)\right)}{\color{blue}{x - \left(x \cdot x\right) \cdot \mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right)}}}{x} \]

   if 2.50000000000000002e77 < x

   1. Initial program 100.0%

      \[\frac{e^{x} - 1}{x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \frac{\color{blue}{x \cdot \left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right)}}{x} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right) \cdot \color{blue}{x}}{x} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right) \cdot \color{blue}{x}}{x} \]

      3. +-commutative N/A

         \[\leadsto \frac{\left(x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right) + 1\right) \cdot x}{x} \]

      4. *-commutative N/A

         \[\leadsto \frac{\left(\left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right) \cdot x + 1\right) \cdot x}{x} \]

      5. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right), x, 1\right) \cdot x}{x} \]

      6. +-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right) + \frac{1}{2}, x, 1\right) \cdot x}{x} \]

      7. *-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\left(\frac{1}{6} + \frac{1}{24} \cdot x\right) \cdot x + \frac{1}{2}, x, 1\right) \cdot x}{x} \]

      8. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{6} + \frac{1}{24} \cdot x, x, \frac{1}{2}\right), x, 1\right) \cdot x}{x} \]

      9. +-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{24} \cdot x + \frac{1}{6}, x, \frac{1}{2}\right), x, 1\right) \cdot x}{x} \]

      10. lower-fma.f64 100.0

          \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right) \cdot x}{x} \]

   5. Applied rewrites 100.0%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right) \cdot x}}{x} \]

   6. Taylor expanded in x around inf

      \[\leadsto \frac{\left({x}^{3} \cdot \left(\frac{1}{24} + \frac{1}{6} \cdot \frac{1}{x}\right)\right) \cdot x}{x} \]
   7. Step-by-step derivation

      1. unpow3 N/A

         \[\leadsto \frac{\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\frac{1}{24} + \frac{1}{6} \cdot \frac{1}{x}\right)\right) \cdot x}{x} \]

      2. pow2 N/A

         \[\leadsto \frac{\left(\left({x}^{2} \cdot x\right) \cdot \left(\frac{1}{24} + \frac{1}{6} \cdot \frac{1}{x}\right)\right) \cdot x}{x} \]

      3. associate-*l* N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(x \cdot \left(\frac{1}{24} + \frac{1}{6} \cdot \frac{1}{x}\right)\right)\right) \cdot x}{x} \]

      4. distribute-lft-in N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(x \cdot \frac{1}{24} + x \cdot \left(\frac{1}{6} \cdot \frac{1}{x}\right)\right)\right) \cdot x}{x} \]

      5. *-commutative N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(\frac{1}{24} \cdot x + x \cdot \left(\frac{1}{6} \cdot \frac{1}{x}\right)\right)\right) \cdot x}{x} \]

      6. *-commutative N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(\frac{1}{24} \cdot x + x \cdot \left(\frac{1}{x} \cdot \frac{1}{6}\right)\right)\right) \cdot x}{x} \]

      7. associate-*r* N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(\frac{1}{24} \cdot x + \left(x \cdot \frac{1}{x}\right) \cdot \frac{1}{6}\right)\right) \cdot x}{x} \]

      8. rgt-mult-inverse N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(\frac{1}{24} \cdot x + 1 \cdot \frac{1}{6}\right)\right) \cdot x}{x} \]

      9. metadata-eval N/A

         \[\leadsto \frac{\left({x}^{2} \cdot \left(\frac{1}{24} \cdot x + \frac{1}{6}\right)\right) \cdot x}{x} \]

      10. lower-*.f64 N/A

          \[\leadsto \frac{\left({x}^{2} \cdot \left(\frac{1}{24} \cdot x + \frac{1}{6}\right)\right) \cdot x}{x} \]

      11. pow2 N/A

          \[\leadsto \frac{\left(\left(x \cdot x\right) \cdot \left(\frac{1}{24} \cdot x + \frac{1}{6}\right)\right) \cdot x}{x} \]

      12. lift-*.f64 N/A

          \[\leadsto \frac{\left(\left(x \cdot x\right) \cdot \left(\frac{1}{24} \cdot x + \frac{1}{6}\right)\right) \cdot x}{x} \]

      13. lift-fma.f64 100.0

          \[\leadsto \frac{\left(\left(x \cdot x\right) \cdot \mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right)\right) \cdot x}{x} \]

   8. Applied rewrites 100.0%

      \[\leadsto \frac{\left(\left(x \cdot x\right) \cdot \mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right)\right) \cdot x}{x} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 6: 69.0% accurate, 3.3× speedup

Math

\[\begin{array}{l} \\ \frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right) \cdot x}{x} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (/
  (*
   (fma (fma (fma 0.041666666666666664 x 0.16666666666666666) x 0.5) x 1.0)
   x)
  x))

C

double code(double x) {
	return (fma(fma(fma(0.041666666666666664, x, 0.16666666666666666), x, 0.5), x, 1.0) * x) / x;
}

Julia

function code(x)
	return Float64(Float64(fma(fma(fma(0.041666666666666664, x, 0.16666666666666666), x, 0.5), x, 1.0) * x) / x)
end

Wolfram

code[x_] := N[(N[(N[(N[(N[(0.041666666666666664 * x + 0.16666666666666666), $MachinePrecision] * x + 0.5), $MachinePrecision] * x + 1.0), $MachinePrecision] * x), $MachinePrecision] / x), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\frac{e^{x} - 1}{x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \frac{\color{blue}{x \cdot \left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right)}}{x} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{\left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right) \cdot \color{blue}{x}}{x} \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{\left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right) \cdot \color{blue}{x}}{x} \]

   3. +-commutative N/A

      \[\leadsto \frac{\left(x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right) + 1\right) \cdot x}{x} \]

   4. *-commutative N/A

      \[\leadsto \frac{\left(\left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right) \cdot x + 1\right) \cdot x}{x} \]

   5. lower-fma.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right), x, 1\right) \cdot x}{x} \]

   6. +-commutative N/A

      \[\leadsto \frac{\mathsf{fma}\left(x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right) + \frac{1}{2}, x, 1\right) \cdot x}{x} \]

   7. *-commutative N/A

      \[\leadsto \frac{\mathsf{fma}\left(\left(\frac{1}{6} + \frac{1}{24} \cdot x\right) \cdot x + \frac{1}{2}, x, 1\right) \cdot x}{x} \]

   8. lower-fma.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{6} + \frac{1}{24} \cdot x, x, \frac{1}{2}\right), x, 1\right) \cdot x}{x} \]

   9. +-commutative N/A

      \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{24} \cdot x + \frac{1}{6}, x, \frac{1}{2}\right), x, 1\right) \cdot x}{x} \]

   10. lower-fma.f64 70.0

       \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right) \cdot x}{x} \]

5. Applied rewrites 70.0%

   \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right) \cdot x}}{x} \]
6. Add Preprocessing

Alternative 7: 68.2% accurate, 3.5× speedup

Math

\[\begin{array}{l} \\ \frac{\mathsf{fma}\left(\left(x \cdot x\right) \cdot 0.041666666666666664, x, 1\right) \cdot x}{x} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (/ (* (fma (* (* x x) 0.041666666666666664) x 1.0) x) x))

C

double code(double x) {
	return (fma(((x * x) * 0.041666666666666664), x, 1.0) * x) / x;
}

Julia

function code(x)
	return Float64(Float64(fma(Float64(Float64(x * x) * 0.041666666666666664), x, 1.0) * x) / x)
end

Wolfram

code[x_] := N[(N[(N[(N[(N[(x * x), $MachinePrecision] * 0.041666666666666664), $MachinePrecision] * x + 1.0), $MachinePrecision] * x), $MachinePrecision] / x), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\frac{e^{x} - 1}{x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \frac{\color{blue}{x \cdot \left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right)}}{x} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{\left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right) \cdot \color{blue}{x}}{x} \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{\left(1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)\right) \cdot \color{blue}{x}}{x} \]

   3. +-commutative N/A

      \[\leadsto \frac{\left(x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right) + 1\right) \cdot x}{x} \]

   4. *-commutative N/A

      \[\leadsto \frac{\left(\left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right) \cdot x + 1\right) \cdot x}{x} \]

   5. lower-fma.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right), x, 1\right) \cdot x}{x} \]

   6. +-commutative N/A

      \[\leadsto \frac{\mathsf{fma}\left(x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right) + \frac{1}{2}, x, 1\right) \cdot x}{x} \]

   7. *-commutative N/A

      \[\leadsto \frac{\mathsf{fma}\left(\left(\frac{1}{6} + \frac{1}{24} \cdot x\right) \cdot x + \frac{1}{2}, x, 1\right) \cdot x}{x} \]

   8. lower-fma.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{6} + \frac{1}{24} \cdot x, x, \frac{1}{2}\right), x, 1\right) \cdot x}{x} \]

   9. +-commutative N/A

      \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{24} \cdot x + \frac{1}{6}, x, \frac{1}{2}\right), x, 1\right) \cdot x}{x} \]

   10. lower-fma.f64 70.0

       \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right) \cdot x}{x} \]

5. Applied rewrites 70.0%

   \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right) \cdot x}}{x} \]

6. Taylor expanded in x around inf

   \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{24} \cdot {x}^{2}, x, 1\right) \cdot x}{x} \]
7. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{\mathsf{fma}\left({x}^{2} \cdot \frac{1}{24}, x, 1\right) \cdot x}{x} \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left({x}^{2} \cdot \frac{1}{24}, x, 1\right) \cdot x}{x} \]

   3. pow2 N/A

      \[\leadsto \frac{\mathsf{fma}\left(\left(x \cdot x\right) \cdot \frac{1}{24}, x, 1\right) \cdot x}{x} \]

   4. lift-*.f64 69.4

      \[\leadsto \frac{\mathsf{fma}\left(\left(x \cdot x\right) \cdot 0.041666666666666664, x, 1\right) \cdot x}{x} \]

8. Applied rewrites 69.4%

   \[\leadsto \frac{\mathsf{fma}\left(\left(x \cdot x\right) \cdot 0.041666666666666664, x, 1\right) \cdot x}{x} \]
9. Add Preprocessing

Alternative 8: 67.1% accurate, 6.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (fma (fma (fma 0.041666666666666664 x 0.16666666666666666) x 0.5) x 1.0))

C

double code(double x) {
	return fma(fma(fma(0.041666666666666664, x, 0.16666666666666666), x, 0.5), x, 1.0);
}

Julia

function code(x)
	return fma(fma(fma(0.041666666666666664, x, 0.16666666666666666), x, 0.5), x, 1.0)
end

Wolfram

code[x_] := N[(N[(N[(0.041666666666666664 * x + 0.16666666666666666), $MachinePrecision] * x + 0.5), $MachinePrecision] * x + 1.0), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\frac{e^{x} - 1}{x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{1 + x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto x \cdot \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right) + \color{blue}{1} \]

   2. *-commutative N/A

      \[\leadsto \left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right)\right) \cdot x + 1 \]

   3. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(\frac{1}{2} + x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right), \color{blue}{x}, 1\right) \]

   4. +-commutative N/A

      \[\leadsto \mathsf{fma}\left(x \cdot \left(\frac{1}{6} + \frac{1}{24} \cdot x\right) + \frac{1}{2}, x, 1\right) \]

   5. *-commutative N/A

      \[\leadsto \mathsf{fma}\left(\left(\frac{1}{6} + \frac{1}{24} \cdot x\right) \cdot x + \frac{1}{2}, x, 1\right) \]

   6. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{6} + \frac{1}{24} \cdot x, x, \frac{1}{2}\right), x, 1\right) \]

   7. +-commutative N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{24} \cdot x + \frac{1}{6}, x, \frac{1}{2}\right), x, 1\right) \]

   8. lower-fma.f64 66.4

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right) \]

5. Applied rewrites 66.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x, 0.16666666666666666\right), x, 0.5\right), x, 1\right)} \]
6. Add Preprocessing

Alternative 9: 63.5% accurate, 6.4× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, 0.5, \mathsf{fma}\left(x \cdot x, 0.16666666666666666, 1\right)\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (fma x 0.5 (fma (* x x) 0.16666666666666666 1.0)))

C

double code(double x) {
	return fma(x, 0.5, fma((x * x), 0.16666666666666666, 1.0));
}

Julia

function code(x)
	return fma(x, 0.5, fma(Float64(x * x), 0.16666666666666666, 1.0))
end

Wolfram

code[x_] := N[(x * 0.5 + N[(N[(x * x), $MachinePrecision] * 0.16666666666666666 + 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\frac{e^{x} - 1}{x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{1 + x \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot x\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto x \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot x\right) + \color{blue}{1} \]

   2. *-commutative N/A

      \[\leadsto \left(\frac{1}{2} + \frac{1}{6} \cdot x\right) \cdot x + 1 \]

   3. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(\frac{1}{2} + \frac{1}{6} \cdot x, \color{blue}{x}, 1\right) \]

   4. +-commutative N/A

      \[\leadsto \mathsf{fma}\left(\frac{1}{6} \cdot x + \frac{1}{2}, x, 1\right) \]

   5. lower-fma.f64 60.9

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, x, 0.5\right), x, 1\right) \]

5. Applied rewrites 60.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, x, 0.5\right), x, 1\right)} \]
6. Step-by-step derivation

   1. lift-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(\frac{1}{6}, x, \frac{1}{2}\right) \cdot x + \color{blue}{1} \]

   2. lift-fma.f64 N/A

      \[\leadsto \left(\frac{1}{6} \cdot x + \frac{1}{2}\right) \cdot x + 1 \]

   3. *-commutative N/A

      \[\leadsto x \cdot \left(\frac{1}{6} \cdot x + \frac{1}{2}\right) + 1 \]

   4. +-commutative N/A

      \[\leadsto x \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot x\right) + 1 \]

   5. distribute-rgt-in N/A

      \[\leadsto \left(\frac{1}{2} \cdot x + \left(\frac{1}{6} \cdot x\right) \cdot x\right) + 1 \]

   6. associate-+l+ N/A

      \[\leadsto \frac{1}{2} \cdot x + \color{blue}{\left(\left(\frac{1}{6} \cdot x\right) \cdot x + 1\right)} \]

   7. *-commutative N/A

      \[\leadsto x \cdot \frac{1}{2} + \left(\color{blue}{\left(\frac{1}{6} \cdot x\right) \cdot x} + 1\right) \]

   8. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{\frac{1}{2}}, \left(\frac{1}{6} \cdot x\right) \cdot x + 1\right) \]

   9. *-commutative N/A

      \[\leadsto \mathsf{fma}\left(x, \frac{1}{2}, x \cdot \left(\frac{1}{6} \cdot x\right) + 1\right) \]

   10. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(x, \frac{1}{2}, x \cdot \left(x \cdot \frac{1}{6}\right) + 1\right) \]

   11. associate-*r* N/A

       \[\leadsto \mathsf{fma}\left(x, \frac{1}{2}, \left(x \cdot x\right) \cdot \frac{1}{6} + 1\right) \]

   12. unpow2 N/A

       \[\leadsto \mathsf{fma}\left(x, \frac{1}{2}, {x}^{2} \cdot \frac{1}{6} + 1\right) \]

   13. lower-fma.f64 N/A

       \[\leadsto \mathsf{fma}\left(x, \frac{1}{2}, \mathsf{fma}\left({x}^{2}, \frac{1}{6}, 1\right)\right) \]

   14. unpow2 N/A

       \[\leadsto \mathsf{fma}\left(x, \frac{1}{2}, \mathsf{fma}\left(x \cdot x, \frac{1}{6}, 1\right)\right) \]

   15. lower-*.f64 61.2

       \[\leadsto \mathsf{fma}\left(x, 0.5, \mathsf{fma}\left(x \cdot x, 0.16666666666666666, 1\right)\right) \]

7. Applied rewrites 61.2%

   \[\leadsto \mathsf{fma}\left(x, \color{blue}{0.5}, \mathsf{fma}\left(x \cdot x, 0.16666666666666666, 1\right)\right) \]
8. Add Preprocessing

Alternative 10: 63.5% accurate, 8.8× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, x, 0.5\right), x, 1\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (fma (fma 0.16666666666666666 x 0.5) x 1.0))

C

double code(double x) {
	return fma(fma(0.16666666666666666, x, 0.5), x, 1.0);
}

Julia

function code(x)
	return fma(fma(0.16666666666666666, x, 0.5), x, 1.0)
end

Wolfram

code[x_] := N[(N[(0.16666666666666666 * x + 0.5), $MachinePrecision] * x + 1.0), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\frac{e^{x} - 1}{x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{1 + x \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot x\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto x \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot x\right) + \color{blue}{1} \]

   2. *-commutative N/A

      \[\leadsto \left(\frac{1}{2} + \frac{1}{6} \cdot x\right) \cdot x + 1 \]

   3. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(\frac{1}{2} + \frac{1}{6} \cdot x, \color{blue}{x}, 1\right) \]

   4. +-commutative N/A

      \[\leadsto \mathsf{fma}\left(\frac{1}{6} \cdot x + \frac{1}{2}, x, 1\right) \]

   5. lower-fma.f64 60.9

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, x, 0.5\right), x, 1\right) \]

5. Applied rewrites 60.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, x, 0.5\right), x, 1\right)} \]
6. Add Preprocessing

Alternative 11: 50.9% accurate, 115.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (x) :precision binary64 1.0)

C

double code(double x) {
	return 1.0;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x)
use fmin_fmax_functions
    real(8), intent (in) :: x
    code = 1.0d0
end function

Java

public static double code(double x) {
	return 1.0;
}

Python

def code(x):
	return 1.0

Julia

function code(x)
	return 1.0
end

MATLAB

function tmp = code(x)
	tmp = 1.0;
end

Wolfram

code[x_] := 1.0

Derivation

1. Initial program 52.1%

   \[\frac{e^{x} - 1}{x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{1} \]
4. Step-by-step derivation

5. Applied rewrites 51.9%

   \[\leadsto \color{blue}{1} \]
6. Add Preprocessing

Developer Target 1: 52.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{x} - 1\\ \mathbf{if}\;x < 1 \land x > -1:\\ \;\;\;\;\frac{t\_0}{\log \left(e^{x}\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{t\_0}{x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (- (exp x) 1.0)))
   (if (and (< x 1.0) (> x -1.0)) (/ t_0 (log (exp x))) (/ t_0 x))))

C

double code(double x) {
	double t_0 = exp(x) - 1.0;
	double tmp;
	if ((x < 1.0) && (x > -1.0)) {
		tmp = t_0 / log(exp(x));
	} else {
		tmp = t_0 / x;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: tmp
    t_0 = exp(x) - 1.0d0
    if ((x < 1.0d0) .and. (x > (-1.0d0))) then
        tmp = t_0 / log(exp(x))
    else
        tmp = t_0 / x
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double t_0 = Math.exp(x) - 1.0;
	double tmp;
	if ((x < 1.0) && (x > -1.0)) {
		tmp = t_0 / Math.log(Math.exp(x));
	} else {
		tmp = t_0 / x;
	}
	return tmp;
}

Python

def code(x):
	t_0 = math.exp(x) - 1.0
	tmp = 0
	if (x < 1.0) and (x > -1.0):
		tmp = t_0 / math.log(math.exp(x))
	else:
		tmp = t_0 / x
	return tmp

Julia

function code(x)
	t_0 = Float64(exp(x) - 1.0)
	tmp = 0.0
	if ((x < 1.0) && (x > -1.0))
		tmp = Float64(t_0 / log(exp(x)));
	else
		tmp = Float64(t_0 / x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	t_0 = exp(x) - 1.0;
	tmp = 0.0;
	if ((x < 1.0) && (x > -1.0))
		tmp = t_0 / log(exp(x));
	else
		tmp = t_0 / x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[Exp[x], $MachinePrecision] - 1.0), $MachinePrecision]}, If[And[Less[x, 1.0], Greater[x, -1.0]], N[(t$95$0 / N[Log[N[Exp[x], $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(t$95$0 / x), $MachinePrecision]]]

Reproduce

herbie shell --seed 2025066 
(FPCore (x)
  :name "Kahan's exp quotient"
  :precision binary64

  :alt
  (! :herbie-platform default (if (and (< x 1) (> x -1)) (/ (- (exp x) 1) (log (exp x))) (/ (- (exp x) 1) x)))

  (/ (- (exp x) 1.0) x))