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

Percentage Accurate: 100.0% → 100.0%

Time: 1.8s

Alternatives: 4

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, 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 2: 62.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 1.85 \cdot 10^{-76} \lor \neg \left(y \leq 3.6 \cdot 10^{-59}\right) \land y \leq 2600000:\\ \;\;\;\;x \cdot 0.1\\ \mathbf{else}:\\ \;\;\;\;y \cdot 0.1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y 1.85e-76) (and (not (<= y 3.6e-59)) (<= y 2600000.0)))
   (* x 0.1)
   (* y 0.1)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= 1.85e-76) || (!(y <= 3.6e-59) && (y <= 2600000.0))) {
		tmp = x * 0.1;
	} else {
		tmp = y * 0.1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= 1.85d-76) .or. (.not. (y <= 3.6d-59)) .and. (y <= 2600000.0d0)) then
        tmp = x * 0.1d0
    else
        tmp = y * 0.1d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= 1.85e-76) || (!(y <= 3.6e-59) && (y <= 2600000.0))) {
		tmp = x * 0.1;
	} else {
		tmp = y * 0.1;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= 1.85e-76) or (not (y <= 3.6e-59) and (y <= 2600000.0)):
		tmp = x * 0.1
	else:
		tmp = y * 0.1
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= 1.85e-76) || (!(y <= 3.6e-59) && (y <= 2600000.0)))
		tmp = Float64(x * 0.1);
	else
		tmp = Float64(y * 0.1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= 1.85e-76) || (~((y <= 3.6e-59)) && (y <= 2600000.0)))
		tmp = x * 0.1;
	else
		tmp = y * 0.1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, 1.85e-76], And[N[Not[LessEqual[y, 3.6e-59]], $MachinePrecision], LessEqual[y, 2600000.0]]], N[(x * 0.1), $MachinePrecision], N[(y * 0.1), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 1.85000000000000006e-76 or 3.6e-59 < y < 2.6e6

   1. Initial program 100.0%

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

      1. *-rgt-identity 100.0%

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

      2. metadata-eval 100.0%

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

      3. associate-*l/ 100.0%

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

      4. associate-/l* 99.5%

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

      5. metadata-eval 99.5%

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

      6. metadata-eval 99.5%

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

   3. Simplified 99.5%

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

   5. Taylor expanded in x around inf 62.6%

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

   if 1.85000000000000006e-76 < y < 3.6e-59 or 2.6e6 < y

   1. Initial program 100.0%

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

      1. *-rgt-identity 100.0%

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

      2. metadata-eval 100.0%

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

      3. associate-*l/ 100.0%

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

      4. associate-/l* 99.5%

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

      5. metadata-eval 99.5%

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

      6. metadata-eval 99.5%

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

   3. Simplified 99.5%

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

   5. Taylor expanded in x around 0 79.2%

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

4. Final simplification 67.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq 1.85 \cdot 10^{-76} \lor \neg \left(y \leq 3.6 \cdot 10^{-59}\right) \land y \leq 2600000:\\ \;\;\;\;x \cdot 0.1\\ \mathbf{else}:\\ \;\;\;\;y \cdot 0.1\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.5% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. *-rgt-identity 100.0%

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

   2. metadata-eval 100.0%

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

   3. associate-*l/ 100.0%

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

   4. associate-/l* 99.5%

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

   5. metadata-eval 99.5%

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

   6. metadata-eval 99.5%

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

3. Simplified 99.5%

   \[\leadsto \color{blue}{\left(x + y\right) \cdot 0.1} \]
4. Add Preprocessing
5. Add Preprocessing

Alternative 4: 50.3% accurate, 1.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. *-rgt-identity 100.0%

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

   2. metadata-eval 100.0%

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

   3. associate-*l/ 100.0%

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

   4. associate-/l* 99.5%

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

   5. metadata-eval 99.5%

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

   6. metadata-eval 99.5%

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

3. Simplified 99.5%

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

5. Taylor expanded in x around inf 51.1%

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

6. Final simplification 51.1%

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

Reproduce

herbie shell --seed 2024100 
(FPCore (x y)
  :name "Text.Parsec.Token:makeTokenParser from parsec-3.1.9, A"
  :precision binary64
  (/ (+ x y) 10.0))