Text.Parsec.Token:makeTokenParser from parsec-3.1.9, A

Percentage Accurate: 100.0% → 100.0%

Time: 6.5s

Alternatives: 7

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{x + y}{10} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (+ x y) 10.0))

C

double code(double x, double y) {
	return (x + y) / 10.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x + y) / 10.0d0
end function

Java

public static double code(double x, double y) {
	return (x + y) / 10.0;
}

Python

def code(x, y):
	return (x + y) / 10.0

Julia

function code(x, y)
	return Float64(Float64(x + y) / 10.0)
end

MATLAB

function tmp = code(x, y)
	tmp = (x + y) / 10.0;
end

Wolfram

code[x_, y_] := N[(N[(x + y), $MachinePrecision] / 10.0), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x + y}{10} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (+ x y) 10.0))

C

double code(double x, double y) {
	return (x + y) / 10.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x + y) / 10.0d0
end function

Java

public static double code(double x, double y) {
	return (x + y) / 10.0;
}

Python

def code(x, y):
	return (x + y) / 10.0

Julia

function code(x, y)
	return Float64(Float64(x + y) / 10.0)
end

MATLAB

function tmp = code(x, y)
	tmp = (x + y) / 10.0;
end

Wolfram

code[x_, y_] := N[(N[(x + y), $MachinePrecision] / 10.0), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \frac{-y}{-10} - \frac{x}{-10} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- (/ (- y) -10.0) (/ x -10.0)))

C

double code(double x, double y) {
	return (-y / -10.0) - (x / -10.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (-y / (-10.0d0)) - (x / (-10.0d0))
end function

Java

public static double code(double x, double y) {
	return (-y / -10.0) - (x / -10.0);
}

Python

def code(x, y):
	return (-y / -10.0) - (x / -10.0)

Julia

function code(x, y)
	return Float64(Float64(Float64(-y) / -10.0) - Float64(x / -10.0))
end

MATLAB

function tmp = code(x, y)
	tmp = (-y / -10.0) - (x / -10.0);
end

Wolfram

code[x_, y_] := N[(N[((-y) / -10.0), $MachinePrecision] - N[(x / -10.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{x + y}{10} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{x + y}{10}} \]

   2. frac-2neg N/A

      \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(\left(x + y\right)\right)}{\mathsf{neg}\left(10\right)}} \]

   3. lift-+.f64 N/A

      \[\leadsto \frac{\mathsf{neg}\left(\color{blue}{\left(x + y\right)}\right)}{\mathsf{neg}\left(10\right)} \]

   4. +-commutative N/A

      \[\leadsto \frac{\mathsf{neg}\left(\color{blue}{\left(y + x\right)}\right)}{\mathsf{neg}\left(10\right)} \]

   5. distribute-neg-in N/A

      \[\leadsto \frac{\color{blue}{\left(\mathsf{neg}\left(y\right)\right) + \left(\mathsf{neg}\left(x\right)\right)}}{\mathsf{neg}\left(10\right)} \]

   6. unsub-neg N/A

      \[\leadsto \frac{\color{blue}{\left(\mathsf{neg}\left(y\right)\right) - x}}{\mathsf{neg}\left(10\right)} \]

   7. div-sub N/A

      \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(y\right)}{\mathsf{neg}\left(10\right)} - \frac{x}{\mathsf{neg}\left(10\right)}} \]

   8. lower--.f64 N/A

      \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(y\right)}{\mathsf{neg}\left(10\right)} - \frac{x}{\mathsf{neg}\left(10\right)}} \]

   9. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\mathsf{neg}\left(y\right)}{\mathsf{neg}\left(10\right)}} - \frac{x}{\mathsf{neg}\left(10\right)} \]

   10. lower-neg.f64 N/A

       \[\leadsto \frac{\color{blue}{-y}}{\mathsf{neg}\left(10\right)} - \frac{x}{\mathsf{neg}\left(10\right)} \]

   11. metadata-eval N/A

       \[\leadsto \frac{-y}{\color{blue}{-10}} - \frac{x}{\mathsf{neg}\left(10\right)} \]

   12. lower-/.f64 N/A

       \[\leadsto \frac{-y}{-10} - \color{blue}{\frac{x}{\mathsf{neg}\left(10\right)}} \]

   13. metadata-eval 100.0

       \[\leadsto \frac{-y}{-10} - \frac{x}{\color{blue}{-10}} \]

4. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\frac{-y}{-10} - \frac{x}{-10}} \]
5. Add Preprocessing

Alternative 2: 51.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x + y \leq -1 \cdot 10^{-266}:\\ \;\;\;\;0.1 \cdot x\\ \mathbf{else}:\\ \;\;\;\;0.1 \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (+ x y) -1e-266) (* 0.1 x) (* 0.1 y)))

C

double code(double x, double y) {
	double tmp;
	if ((x + y) <= -1e-266) {
		tmp = 0.1 * x;
	} else {
		tmp = 0.1 * y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((x + y) <= (-1d-266)) then
        tmp = 0.1d0 * x
    else
        tmp = 0.1d0 * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((x + y) <= -1e-266) {
		tmp = 0.1 * x;
	} else {
		tmp = 0.1 * y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (x + y) <= -1e-266:
		tmp = 0.1 * x
	else:
		tmp = 0.1 * y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(x + y) <= -1e-266)
		tmp = Float64(0.1 * x);
	else
		tmp = Float64(0.1 * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x + y) <= -1e-266)
		tmp = 0.1 * x;
	else
		tmp = 0.1 * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(x + y), $MachinePrecision], -1e-266], N[(0.1 * x), $MachinePrecision], N[(0.1 * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 x y) < -9.9999999999999998e-267

   1. Initial program 100.0%

      \[\frac{x + y}{10} \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{\frac{1}{10} \cdot x} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{x \cdot \frac{1}{10}} \]

      2. lower-*.f64 53.4

         \[\leadsto \color{blue}{x \cdot 0.1} \]

   5. Applied rewrites 53.4%

      \[\leadsto \color{blue}{x \cdot 0.1} \]

   if -9.9999999999999998e-267 < (+.f64 x y)

   1. Initial program 100.0%

      \[\frac{x + y}{10} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{\frac{1}{10} \cdot y} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \color{blue}{y \cdot \frac{1}{10}} \]

      2. lower-*.f64 50.1

         \[\leadsto \color{blue}{y \cdot 0.1} \]

   5. Applied rewrites 50.1%

      \[\leadsto \color{blue}{y \cdot 0.1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 51.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x + y \leq -1 \cdot 10^{-266}:\\ \;\;\;\;0.1 \cdot x\\ \mathbf{else}:\\ \;\;\;\;0.1 \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x + y}{10} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ (+ x y) 10.0))

C

double code(double x, double y) {
	return (x + y) / 10.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x + y) / 10.0d0
end function

Java

public static double code(double x, double y) {
	return (x + y) / 10.0;
}

Python

def code(x, y):
	return (x + y) / 10.0

Julia

function code(x, y)
	return Float64(Float64(x + y) / 10.0)
end

MATLAB

function tmp = code(x, y)
	tmp = (x + y) / 10.0;
end

Wolfram

code[x_, y_] := N[(N[(x + y), $MachinePrecision] / 10.0), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{x + y}{10} \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 4: 99.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ 0.01 \cdot \left(\left(x + y\right) \cdot 10\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 0.01 (* (+ x y) 10.0)))

C

double code(double x, double y) {
	return 0.01 * ((x + y) * 10.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 0.01d0 * ((x + y) * 10.0d0)
end function

Java

public static double code(double x, double y) {
	return 0.01 * ((x + y) * 10.0);
}

Python

def code(x, y):
	return 0.01 * ((x + y) * 10.0)

Julia

function code(x, y)
	return Float64(0.01 * Float64(Float64(x + y) * 10.0))
end

MATLAB

function tmp = code(x, y)
	tmp = 0.01 * ((x + y) * 10.0);
end

Wolfram

code[x_, y_] := N[(0.01 * N[(N[(x + y), $MachinePrecision] * 10.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{x + y}{10} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{x + y}{10}} \]

   2. clear-num N/A

      \[\leadsto \color{blue}{\frac{1}{\frac{10}{x + y}}} \]

   3. associate-/r/ N/A

      \[\leadsto \color{blue}{\frac{1}{10} \cdot \left(x + y\right)} \]

   4. lift-+.f64 N/A

      \[\leadsto \frac{1}{10} \cdot \color{blue}{\left(x + y\right)} \]

   5. +-commutative N/A

      \[\leadsto \frac{1}{10} \cdot \color{blue}{\left(y + x\right)} \]

   6. distribute-rgt-in N/A

      \[\leadsto \color{blue}{y \cdot \frac{1}{10} + x \cdot \frac{1}{10}} \]

   7. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{1}{10}, x \cdot \frac{1}{10}\right)} \]

   8. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left(y, \color{blue}{\frac{1}{10}}, x \cdot \frac{1}{10}\right) \]

   9. lower-*.f64 N/A

      \[\leadsto \mathsf{fma}\left(y, \frac{1}{10}, \color{blue}{x \cdot \frac{1}{10}}\right) \]

   10. metadata-eval 99.4

       \[\leadsto \mathsf{fma}\left(y, 0.1, x \cdot \color{blue}{0.1}\right) \]

4. Applied rewrites 99.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, 0.1, x \cdot 0.1\right)} \]
5. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \mathsf{fma}\left(y, \frac{1}{10}, \color{blue}{x \cdot \frac{1}{10}}\right) \]

   2. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left(y, \frac{1}{10}, x \cdot \color{blue}{\left(\mathsf{neg}\left(\frac{-1}{10}\right)\right)}\right) \]

   3. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left(y, \frac{1}{10}, x \cdot \left(\mathsf{neg}\left(\color{blue}{\frac{1}{-10}}\right)\right)\right) \]

   4. distribute-rgt-neg-in N/A

      \[\leadsto \mathsf{fma}\left(y, \frac{1}{10}, \color{blue}{\mathsf{neg}\left(x \cdot \frac{1}{-10}\right)}\right) \]

   5. div-inv N/A

      \[\leadsto \mathsf{fma}\left(y, \frac{1}{10}, \mathsf{neg}\left(\color{blue}{\frac{x}{-10}}\right)\right) \]

   6. distribute-neg-frac2 N/A

      \[\leadsto \mathsf{fma}\left(y, \frac{1}{10}, \color{blue}{\frac{x}{\mathsf{neg}\left(-10\right)}}\right) \]

   7. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left(y, \frac{1}{10}, \frac{x}{\color{blue}{10}}\right) \]

   8. lower-/.f64 99.7

      \[\leadsto \mathsf{fma}\left(y, 0.1, \color{blue}{\frac{x}{10}}\right) \]

6. Applied rewrites 99.7%

   \[\leadsto \mathsf{fma}\left(y, 0.1, \color{blue}{\frac{x}{10}}\right) \]
7. Step-by-step derivation

   1. lift-fma.f64 N/A

      \[\leadsto \color{blue}{y \cdot \frac{1}{10} + \frac{x}{10}} \]

   2. lift-/.f64 N/A

      \[\leadsto y \cdot \frac{1}{10} + \color{blue}{\frac{x}{10}} \]

   3. div-inv N/A

      \[\leadsto y \cdot \frac{1}{10} + \color{blue}{x \cdot \frac{1}{10}} \]

   4. metadata-eval N/A

      \[\leadsto y \cdot \frac{1}{10} + x \cdot \color{blue}{\frac{1}{10}} \]

   5. distribute-rgt-out N/A

      \[\leadsto \color{blue}{\frac{1}{10} \cdot \left(y + x\right)} \]

   6. +-commutative N/A

      \[\leadsto \frac{1}{10} \cdot \color{blue}{\left(x + y\right)} \]

   7. metadata-eval N/A

      \[\leadsto \color{blue}{\left(\frac{1}{100} \cdot 10\right)} \cdot \left(x + y\right) \]

   8. associate-*r* N/A

      \[\leadsto \color{blue}{\frac{1}{100} \cdot \left(10 \cdot \left(x + y\right)\right)} \]

   9. distribute-lft-out N/A

      \[\leadsto \frac{1}{100} \cdot \color{blue}{\left(10 \cdot x + 10 \cdot y\right)} \]

   10. lift-*.f64 N/A

       \[\leadsto \frac{1}{100} \cdot \left(\color{blue}{10 \cdot x} + 10 \cdot y\right) \]

   11. metadata-eval N/A

       \[\leadsto \frac{1}{100} \cdot \left(10 \cdot x + \color{blue}{\left(\mathsf{neg}\left(-10\right)\right)} \cdot y\right) \]

   12. distribute-lft-neg-in N/A

       \[\leadsto \frac{1}{100} \cdot \left(10 \cdot x + \color{blue}{\left(\mathsf{neg}\left(-10 \cdot y\right)\right)}\right) \]

   13. distribute-rgt-neg-out N/A

       \[\leadsto \frac{1}{100} \cdot \left(10 \cdot x + \color{blue}{-10 \cdot \left(\mathsf{neg}\left(y\right)\right)}\right) \]

   14. lift-neg.f64 N/A

       \[\leadsto \frac{1}{100} \cdot \left(10 \cdot x + -10 \cdot \color{blue}{\left(-y\right)}\right) \]

   15. +-commutative N/A

       \[\leadsto \frac{1}{100} \cdot \color{blue}{\left(-10 \cdot \left(-y\right) + 10 \cdot x\right)} \]

   16. lift-fma.f64 N/A

       \[\leadsto \frac{1}{100} \cdot \color{blue}{\mathsf{fma}\left(-10, -y, 10 \cdot x\right)} \]

   17. *-commutative N/A

       \[\leadsto \color{blue}{\mathsf{fma}\left(-10, -y, 10 \cdot x\right) \cdot \frac{1}{100}} \]

   18. lift-*.f64 99.5

       \[\leadsto \color{blue}{\mathsf{fma}\left(-10, -y, 10 \cdot x\right) \cdot 0.01} \]

   19. lift-fma.f64 N/A

       \[\leadsto \color{blue}{\left(-10 \cdot \left(-y\right) + 10 \cdot x\right)} \cdot \frac{1}{100} \]

   20. +-commutative N/A

       \[\leadsto \color{blue}{\left(10 \cdot x + -10 \cdot \left(-y\right)\right)} \cdot \frac{1}{100} \]

   21. lift-*.f64 N/A

       \[\leadsto \left(\color{blue}{10 \cdot x} + -10 \cdot \left(-y\right)\right) \cdot \frac{1}{100} \]

   22. lift-neg.f64 N/A

       \[\leadsto \left(10 \cdot x + -10 \cdot \color{blue}{\left(\mathsf{neg}\left(y\right)\right)}\right) \cdot \frac{1}{100} \]

   23. distribute-rgt-neg-out N/A

       \[\leadsto \left(10 \cdot x + \color{blue}{\left(\mathsf{neg}\left(-10 \cdot y\right)\right)}\right) \cdot \frac{1}{100} \]

   24. distribute-lft-neg-in N/A

       \[\leadsto \left(10 \cdot x + \color{blue}{\left(\mathsf{neg}\left(-10\right)\right) \cdot y}\right) \cdot \frac{1}{100} \]

   25. metadata-eval N/A

       \[\leadsto \left(10 \cdot x + \color{blue}{10} \cdot y\right) \cdot \frac{1}{100} \]

   26. distribute-lft-out N/A

       \[\leadsto \color{blue}{\left(10 \cdot \left(x + y\right)\right)} \cdot \frac{1}{100} \]

   27. lower-*.f64 N/A

       \[\leadsto \color{blue}{\left(10 \cdot \left(x + y\right)\right)} \cdot \frac{1}{100} \]

   28. +-commutative N/A

       \[\leadsto \left(10 \cdot \color{blue}{\left(y + x\right)}\right) \cdot \frac{1}{100} \]

   29. lower-+.f64 99.5

       \[\leadsto \left(10 \cdot \color{blue}{\left(y + x\right)}\right) \cdot 0.01 \]

8. Applied rewrites 99.5%

   \[\leadsto \color{blue}{\left(10 \cdot \left(y + x\right)\right) \cdot 0.01} \]

9. Final simplification 99.5%

   \[\leadsto 0.01 \cdot \left(\left(x + y\right) \cdot 10\right) \]
10. Add Preprocessing

Alternative 5: 99.4% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y, 0.1, 0.1 \cdot x\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma y 0.1 (* 0.1 x)))

C

double code(double x, double y) {
	return fma(y, 0.1, (0.1 * x));
}

Julia

function code(x, y)
	return fma(y, 0.1, Float64(0.1 * x))
end

Wolfram

code[x_, y_] := N[(y * 0.1 + N[(0.1 * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{x + y}{10} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{x + y}{10}} \]

   2. clear-num N/A

      \[\leadsto \color{blue}{\frac{1}{\frac{10}{x + y}}} \]

   3. associate-/r/ N/A

      \[\leadsto \color{blue}{\frac{1}{10} \cdot \left(x + y\right)} \]

   4. lift-+.f64 N/A

      \[\leadsto \frac{1}{10} \cdot \color{blue}{\left(x + y\right)} \]

   5. +-commutative N/A

      \[\leadsto \frac{1}{10} \cdot \color{blue}{\left(y + x\right)} \]

   6. distribute-rgt-in N/A

      \[\leadsto \color{blue}{y \cdot \frac{1}{10} + x \cdot \frac{1}{10}} \]

   7. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{1}{10}, x \cdot \frac{1}{10}\right)} \]

   8. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left(y, \color{blue}{\frac{1}{10}}, x \cdot \frac{1}{10}\right) \]

   9. lower-*.f64 N/A

      \[\leadsto \mathsf{fma}\left(y, \frac{1}{10}, \color{blue}{x \cdot \frac{1}{10}}\right) \]

   10. metadata-eval 99.4

       \[\leadsto \mathsf{fma}\left(y, 0.1, x \cdot \color{blue}{0.1}\right) \]

4. Applied rewrites 99.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, 0.1, x \cdot 0.1\right)} \]

5. Final simplification 99.4%

   \[\leadsto \mathsf{fma}\left(y, 0.1, 0.1 \cdot x\right) \]
6. Add Preprocessing

Alternative 6: 99.4% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ 0.1 \cdot \left(x + y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 0.1 (+ x y)))

C

double code(double x, double y) {
	return 0.1 * (x + y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 0.1d0 * (x + y)
end function

Java

public static double code(double x, double y) {
	return 0.1 * (x + y);
}

Python

def code(x, y):
	return 0.1 * (x + y)

Julia

function code(x, y)
	return Float64(0.1 * Float64(x + y))
end

MATLAB

function tmp = code(x, y)
	tmp = 0.1 * (x + y);
end

Wolfram

code[x_, y_] := N[(0.1 * N[(x + y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{x + y}{10} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{x + y}{10}} \]

   2. div-inv N/A

      \[\leadsto \color{blue}{\left(x + y\right) \cdot \frac{1}{10}} \]

   3. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(x + y\right) \cdot \frac{1}{10}} \]

   4. lift-+.f64 N/A

      \[\leadsto \color{blue}{\left(x + y\right)} \cdot \frac{1}{10} \]

   5. +-commutative N/A

      \[\leadsto \color{blue}{\left(y + x\right)} \cdot \frac{1}{10} \]

   6. lower-+.f64 N/A

      \[\leadsto \color{blue}{\left(y + x\right)} \cdot \frac{1}{10} \]

   7. metadata-eval 99.4

      \[\leadsto \left(y + x\right) \cdot \color{blue}{0.1} \]

4. Applied rewrites 99.4%

   \[\leadsto \color{blue}{\left(y + x\right) \cdot 0.1} \]

5. Final simplification 99.4%

   \[\leadsto 0.1 \cdot \left(x + y\right) \]
6. Add Preprocessing

Alternative 7: 50.6% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ 0.1 \cdot x \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 0.1 x))

C

double code(double x, double y) {
	return 0.1 * x;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 0.1d0 * x
end function

Java

public static double code(double x, double y) {
	return 0.1 * x;
}

Python

def code(x, y):
	return 0.1 * x

Julia

function code(x, y)
	return Float64(0.1 * x)
end

MATLAB

function tmp = code(x, y)
	tmp = 0.1 * x;
end

Wolfram

code[x_, y_] := N[(0.1 * x), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{x + y}{10} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

   \[\leadsto \color{blue}{\frac{1}{10} \cdot x} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \color{blue}{x \cdot \frac{1}{10}} \]

   2. lower-*.f64 51.6

      \[\leadsto \color{blue}{x \cdot 0.1} \]

5. Applied rewrites 51.6%

   \[\leadsto \color{blue}{x \cdot 0.1} \]

6. Final simplification 51.6%

   \[\leadsto 0.1 \cdot x \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024249 
(FPCore (x y)
  :name "Text.Parsec.Token:makeTokenParser from parsec-3.1.9, A"
  :precision binary64
  (/ (+ x y) 10.0))