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

Percentage Accurate: 100.0% → 100.0%

Time: 8.3s

Alternatives: 5

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, 0.7× 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(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

4. Applied egg-rr 0

   \[\leadsto expr\]

6. Applied egg-rr 0

   \[\leadsto expr\]
7. Add Preprocessing

Alternative 2: 62.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 5.2 \cdot 10^{-93}:\\ \;\;\;\;\frac{x}{10}\\ \mathbf{elif}\;y \leq 9 \cdot 10^{-72}:\\ \;\;\;\;\frac{y}{10}\\ \mathbf{elif}\;y \leq 7.2 \cdot 10^{-33}:\\ \;\;\;\;\frac{x}{10}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{10}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y 5.2e-93)
   (/ x 10.0)
   (if (<= y 9e-72) (/ y 10.0) (if (<= y 7.2e-33) (/ x 10.0) (/ y 10.0)))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 5.2e-93) {
		tmp = x / 10.0;
	} else if (y <= 9e-72) {
		tmp = y / 10.0;
	} else if (y <= 7.2e-33) {
		tmp = x / 10.0;
	} else {
		tmp = y / 10.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= 5.2d-93) then
        tmp = x / 10.0d0
    else if (y <= 9d-72) then
        tmp = y / 10.0d0
    else if (y <= 7.2d-33) then
        tmp = x / 10.0d0
    else
        tmp = y / 10.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 5.2e-93) {
		tmp = x / 10.0;
	} else if (y <= 9e-72) {
		tmp = y / 10.0;
	} else if (y <= 7.2e-33) {
		tmp = x / 10.0;
	} else {
		tmp = y / 10.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 5.2e-93:
		tmp = x / 10.0
	elif y <= 9e-72:
		tmp = y / 10.0
	elif y <= 7.2e-33:
		tmp = x / 10.0
	else:
		tmp = y / 10.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 5.2e-93)
		tmp = Float64(x / 10.0);
	elseif (y <= 9e-72)
		tmp = Float64(y / 10.0);
	elseif (y <= 7.2e-33)
		tmp = Float64(x / 10.0);
	else
		tmp = Float64(y / 10.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 5.2e-93)
		tmp = x / 10.0;
	elseif (y <= 9e-72)
		tmp = y / 10.0;
	elseif (y <= 7.2e-33)
		tmp = x / 10.0;
	else
		tmp = y / 10.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 5.2e-93], N[(x / 10.0), $MachinePrecision], If[LessEqual[y, 9e-72], N[(y / 10.0), $MachinePrecision], If[LessEqual[y, 7.2e-33], N[(x / 10.0), $MachinePrecision], N[(y / 10.0), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if y < 5.1999999999999997e-93 or 9e-72 < y < 7.20000000000000068e-33

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if 5.1999999999999997e-93 < y < 9e-72 or 7.20000000000000068e-33 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 61.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.02 \cdot 10^{-95}:\\ \;\;\;\;\frac{x}{10}\\ \mathbf{elif}\;y \leq 9.5 \cdot 10^{-72}:\\ \;\;\;\;0.1 \cdot y\\ \mathbf{elif}\;y \leq 4.1 \cdot 10^{-33}:\\ \;\;\;\;\frac{x}{10}\\ \mathbf{else}:\\ \;\;\;\;0.1 \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y 1.02e-95)
   (/ x 10.0)
   (if (<= y 9.5e-72) (* 0.1 y) (if (<= y 4.1e-33) (/ x 10.0) (* 0.1 y)))))

C

double code(double x, double y) {
	double tmp;
	if (y <= 1.02e-95) {
		tmp = x / 10.0;
	} else if (y <= 9.5e-72) {
		tmp = 0.1 * y;
	} else if (y <= 4.1e-33) {
		tmp = x / 10.0;
	} 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 (y <= 1.02d-95) then
        tmp = x / 10.0d0
    else if (y <= 9.5d-72) then
        tmp = 0.1d0 * y
    else if (y <= 4.1d-33) then
        tmp = x / 10.0d0
    else
        tmp = 0.1d0 * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= 1.02e-95) {
		tmp = x / 10.0;
	} else if (y <= 9.5e-72) {
		tmp = 0.1 * y;
	} else if (y <= 4.1e-33) {
		tmp = x / 10.0;
	} else {
		tmp = 0.1 * y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= 1.02e-95:
		tmp = x / 10.0
	elif y <= 9.5e-72:
		tmp = 0.1 * y
	elif y <= 4.1e-33:
		tmp = x / 10.0
	else:
		tmp = 0.1 * y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 1.02e-95)
		tmp = Float64(x / 10.0);
	elseif (y <= 9.5e-72)
		tmp = Float64(0.1 * y);
	elseif (y <= 4.1e-33)
		tmp = Float64(x / 10.0);
	else
		tmp = Float64(0.1 * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= 1.02e-95)
		tmp = x / 10.0;
	elseif (y <= 9.5e-72)
		tmp = 0.1 * y;
	elseif (y <= 4.1e-33)
		tmp = x / 10.0;
	else
		tmp = 0.1 * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, 1.02e-95], N[(x / 10.0), $MachinePrecision], If[LessEqual[y, 9.5e-72], N[(0.1 * y), $MachinePrecision], If[LessEqual[y, 4.1e-33], N[(x / 10.0), $MachinePrecision], N[(0.1 * y), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if y < 1.01999999999999995e-95 or 9.4999999999999998e-72 < y < 4.1e-33

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if 1.01999999999999995e-95 < y < 9.4999999999999998e-72 or 4.1e-33 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 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 5: 49.9% accurate, 1.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0 0

   \[\leadsto expr\]

5. Simplified 0

   \[\leadsto expr\]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024110 
(FPCore (x y)
  :name "Text.Parsec.Token:makeTokenParser from parsec-3.1.9, A"
  :precision binary64
  (/ (+ x y) 10.0))