Data.Colour.CIE:cieLABView from colour-2.3.3, C

Percentage Accurate: 99.9% → 100.0%

Time: 2.4s

Alternatives: 5

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ 200 \cdot \left(x - y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 200.0 (- x y)))

C

double code(double x, double y) {
	return 200.0 * (x - y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 200.0d0 * (x - y)
end function

Java

public static double code(double x, double y) {
	return 200.0 * (x - y);
}

Python

def code(x, y):
	return 200.0 * (x - y)

Julia

function code(x, y)
	return Float64(200.0 * Float64(x - y))
end

MATLAB

function tmp = code(x, y)
	tmp = 200.0 * (x - y);
end

Wolfram

code[x_, y_] := N[(200.0 * N[(x - y), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 200 \cdot \left(x - y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 200.0 (- x y)))

C

double code(double x, double y) {
	return 200.0 * (x - y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 200.0d0 * (x - y)
end function

Java

public static double code(double x, double y) {
	return 200.0 * (x - y);
}

Python

def code(x, y):
	return 200.0 * (x - y)

Julia

function code(x, y)
	return Float64(200.0 * Float64(x - y))
end

MATLAB

function tmp = code(x, y)
	tmp = 200.0 * (x - y);
end

Wolfram

code[x_, y_] := N[(200.0 * N[(x - y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, 200, y \cdot -200\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma x 200.0 (* y -200.0)))

C

double code(double x, double y) {
	return fma(x, 200.0, (y * -200.0));
}

Julia

function code(x, y)
	return fma(x, 200.0, Float64(y * -200.0))
end

Wolfram

code[x_, y_] := N[(x * 200.0 + N[(y * -200.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[200 \cdot \left(x - y\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{200 \cdot \left(x - y\right)} \]

   2. lift--.f64 N/A

      \[\leadsto 200 \cdot \color{blue}{\left(x - y\right)} \]

   3. sub-neg N/A

      \[\leadsto 200 \cdot \color{blue}{\left(x + \left(\mathsf{neg}\left(y\right)\right)\right)} \]

   4. distribute-rgt-in N/A

      \[\leadsto \color{blue}{x \cdot 200 + \left(\mathsf{neg}\left(y\right)\right) \cdot 200} \]

   5. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, 200, \left(\mathsf{neg}\left(y\right)\right) \cdot 200\right)} \]

   6. *-commutative N/A

      \[\leadsto \mathsf{fma}\left(x, 200, \color{blue}{200 \cdot \left(\mathsf{neg}\left(y\right)\right)}\right) \]

   7. neg-mul-1 N/A

      \[\leadsto \mathsf{fma}\left(x, 200, 200 \cdot \color{blue}{\left(-1 \cdot y\right)}\right) \]

   8. associate-*r* N/A

      \[\leadsto \mathsf{fma}\left(x, 200, \color{blue}{\left(200 \cdot -1\right) \cdot y}\right) \]

   9. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left(x, 200, \color{blue}{-200} \cdot y\right) \]

   10. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(x, 200, \color{blue}{\left(\mathsf{neg}\left(200\right)\right)} \cdot y\right) \]

   11. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(x, 200, \color{blue}{\left(\mathsf{neg}\left(200\right)\right) \cdot y}\right) \]

   12. metadata-eval 100.0

       \[\leadsto \mathsf{fma}\left(x, 200, \color{blue}{-200} \cdot y\right) \]

4. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, 200, -200 \cdot y\right)} \]

5. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(x, 200, y \cdot -200\right) \]
6. Add Preprocessing

Alternative 2: 74.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -4.4 \cdot 10^{+38}:\\ \;\;\;\;y \cdot -200\\ \mathbf{elif}\;y \leq 1.65 \cdot 10^{-13}:\\ \;\;\;\;200 \cdot x\\ \mathbf{else}:\\ \;\;\;\;y \cdot -200\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -4.4e+38)
   (* y -200.0)
   (if (<= y 1.65e-13) (* 200.0 x) (* y -200.0))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -4.4e+38) {
		tmp = y * -200.0;
	} else if (y <= 1.65e-13) {
		tmp = 200.0 * x;
	} else {
		tmp = y * -200.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-4.4d+38)) then
        tmp = y * (-200.0d0)
    else if (y <= 1.65d-13) then
        tmp = 200.0d0 * x
    else
        tmp = y * (-200.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -4.4e+38) {
		tmp = y * -200.0;
	} else if (y <= 1.65e-13) {
		tmp = 200.0 * x;
	} else {
		tmp = y * -200.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -4.4e+38:
		tmp = y * -200.0
	elif y <= 1.65e-13:
		tmp = 200.0 * x
	else:
		tmp = y * -200.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -4.4e+38)
		tmp = Float64(y * -200.0);
	elseif (y <= 1.65e-13)
		tmp = Float64(200.0 * x);
	else
		tmp = Float64(y * -200.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -4.4e+38)
		tmp = y * -200.0;
	elseif (y <= 1.65e-13)
		tmp = 200.0 * x;
	else
		tmp = y * -200.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -4.4e+38], N[(y * -200.0), $MachinePrecision], If[LessEqual[y, 1.65e-13], N[(200.0 * x), $MachinePrecision], N[(y * -200.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -4.40000000000000013e38 or 1.65e-13 < y

   1. Initial program 99.9%

      \[200 \cdot \left(x - y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{-200 \cdot y} \]
   4. Step-by-step derivation

      1. lower-*.f64 82.1

         \[\leadsto \color{blue}{-200 \cdot y} \]

   5. Applied rewrites 82.1%

      \[\leadsto \color{blue}{-200 \cdot y} \]

   if -4.40000000000000013e38 < y < 1.65e-13

   1. Initial program 99.9%

      \[200 \cdot \left(x - y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{200 \cdot x} \]
   4. Step-by-step derivation

      1. lower-*.f64 80.7

         \[\leadsto \color{blue}{200 \cdot x} \]

   5. Applied rewrites 80.7%

      \[\leadsto \color{blue}{200 \cdot x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 81.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4.4 \cdot 10^{+38}:\\ \;\;\;\;y \cdot -200\\ \mathbf{elif}\;y \leq 1.65 \cdot 10^{-13}:\\ \;\;\;\;200 \cdot x\\ \mathbf{else}:\\ \;\;\;\;y \cdot -200\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 100.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(-200, y, 200 \cdot x\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma -200.0 y (* 200.0 x)))

C

double code(double x, double y) {
	return fma(-200.0, y, (200.0 * x));
}

Julia

function code(x, y)
	return fma(-200.0, y, Float64(200.0 * x))
end

Wolfram

code[x_, y_] := N[(-200.0 * y + N[(200.0 * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[200 \cdot \left(x - y\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{200 \cdot \left(x - y\right)} \]

   2. lift--.f64 N/A

      \[\leadsto 200 \cdot \color{blue}{\left(x - y\right)} \]

   3. sub-neg N/A

      \[\leadsto 200 \cdot \color{blue}{\left(x + \left(\mathsf{neg}\left(y\right)\right)\right)} \]

   4. +-commutative N/A

      \[\leadsto 200 \cdot \color{blue}{\left(\left(\mathsf{neg}\left(y\right)\right) + x\right)} \]

   5. distribute-lft-in N/A

      \[\leadsto \color{blue}{200 \cdot \left(\mathsf{neg}\left(y\right)\right) + 200 \cdot x} \]

   6. neg-mul-1 N/A

      \[\leadsto 200 \cdot \color{blue}{\left(-1 \cdot y\right)} + 200 \cdot x \]

   7. associate-*r* N/A

      \[\leadsto \color{blue}{\left(200 \cdot -1\right) \cdot y} + 200 \cdot x \]

   8. metadata-eval N/A

      \[\leadsto \color{blue}{-200} \cdot y + 200 \cdot x \]

   9. metadata-eval N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(200\right)\right)} \cdot y + 200 \cdot x \]

   10. *-commutative N/A

       \[\leadsto \left(\mathsf{neg}\left(200\right)\right) \cdot y + \color{blue}{x \cdot 200} \]

   11. lower-fma.f64 N/A

       \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{neg}\left(200\right), y, x \cdot 200\right)} \]

   12. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{-200}, y, x \cdot 200\right) \]

   13. lower-*.f64 100.0

       \[\leadsto \mathsf{fma}\left(-200, y, \color{blue}{x \cdot 200}\right) \]

4. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(-200, y, x \cdot 200\right)} \]

5. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(-200, y, 200 \cdot x\right) \]
6. Add Preprocessing

Alternative 4: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x - y\right) \cdot 200 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (- x y) 200.0))

C

double code(double x, double y) {
	return (x - y) * 200.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x - y) * 200.0d0
end function

Java

public static double code(double x, double y) {
	return (x - y) * 200.0;
}

Python

def code(x, y):
	return (x - y) * 200.0

Julia

function code(x, y)
	return Float64(Float64(x - y) * 200.0)
end

MATLAB

function tmp = code(x, y)
	tmp = (x - y) * 200.0;
end

Wolfram

code[x_, y_] := N[(N[(x - y), $MachinePrecision] * 200.0), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[200 \cdot \left(x - y\right) \]
2. Add Preprocessing

3. Final simplification 99.9%

   \[\leadsto \left(x - y\right) \cdot 200 \]
4. Add Preprocessing

Alternative 5: 49.7% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ y \cdot -200 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* y -200.0))

C

double code(double x, double y) {
	return y * -200.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = y * (-200.0d0)
end function

Java

public static double code(double x, double y) {
	return y * -200.0;
}

Python

def code(x, y):
	return y * -200.0

Julia

function code(x, y)
	return Float64(y * -200.0)
end

MATLAB

function tmp = code(x, y)
	tmp = y * -200.0;
end

Wolfram

code[x_, y_] := N[(y * -200.0), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[200 \cdot \left(x - y\right) \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{-200 \cdot y} \]
4. Step-by-step derivation

   1. lower-*.f64 49.7

      \[\leadsto \color{blue}{-200 \cdot y} \]

5. Applied rewrites 49.7%

   \[\leadsto \color{blue}{-200 \cdot y} \]

6. Final simplification 49.7%

   \[\leadsto y \cdot -200 \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024298 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLABView from colour-2.3.3, C"
  :precision binary64
  (* 200.0 (- x y)))