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

Percentage Accurate: 100.0% → 100.0%

Time: 3.7s

Alternatives: 4

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ 500 \cdot \left(x - y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 500.0 (- x y)))

C

double code(double x, double y) {
	return 500.0 * (x - y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 500.0d0 * (x - y)
end function

Java

public static double code(double x, double y) {
	return 500.0 * (x - y);
}

Python

def code(x, y):
	return 500.0 * (x - y)

Julia

function code(x, y)
	return Float64(500.0 * Float64(x - y))
end

MATLAB

function tmp = code(x, y)
	tmp = 500.0 * (x - y);
end

Wolfram

code[x_, y_] := N[(500.0 * N[(x - y), $MachinePrecision]), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 500 \cdot \left(x - y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 500.0 (- x y)))

C

double code(double x, double y) {
	return 500.0 * (x - y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 500.0d0 * (x - y)
end function

Java

public static double code(double x, double y) {
	return 500.0 * (x - y);
}

Python

def code(x, y):
	return 500.0 * (x - y)

Julia

function code(x, y)
	return Float64(500.0 * Float64(x - y))
end

MATLAB

function tmp = code(x, y)
	tmp = 500.0 * (x - y);
end

Wolfram

code[x_, y_] := N[(500.0 * N[(x - y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y, -500, x \cdot 500\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma y -500.0 (* x 500.0)))

C

double code(double x, double y) {
	return fma(y, -500.0, (x * 500.0));
}

Julia

function code(x, y)
	return fma(y, -500.0, Float64(x * 500.0))
end

Wolfram

code[x_, y_] := N[(y * -500.0 + N[(x * 500.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[500 \cdot \left(x - y\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. sub-neg N/A

      \[\leadsto 500 \cdot \color{blue}{\left(x + \left(\mathsf{neg}\left(y\right)\right)\right)} \]

   2. +-commutative N/A

      \[\leadsto 500 \cdot \color{blue}{\left(\left(\mathsf{neg}\left(y\right)\right) + x\right)} \]

   3. distribute-rgt-in N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y\right)\right) \cdot 500 + x \cdot 500} \]

   4. distribute-lft-neg-out N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(y \cdot 500\right)\right)} + x \cdot 500 \]

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

      \[\leadsto \color{blue}{y \cdot \left(\mathsf{neg}\left(500\right)\right)} + x \cdot 500 \]

   6. metadata-eval N/A

      \[\leadsto y \cdot \color{blue}{-500} + x \cdot 500 \]

   7. metadata-eval N/A

      \[\leadsto y \cdot \color{blue}{\left(500 \cdot -1\right)} + x \cdot 500 \]

   8. accelerator-lowering-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, 500 \cdot -1, x \cdot 500\right)} \]

   9. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left(y, \color{blue}{-500}, x \cdot 500\right) \]

   10. *-lowering-*.f64 100.0

       \[\leadsto \mathsf{fma}\left(y, -500, \color{blue}{x \cdot 500}\right) \]

4. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, -500, x \cdot 500\right)} \]
5. Add Preprocessing

Alternative 2: 73.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.6 \cdot 10^{-96}:\\ \;\;\;\;y \cdot -500\\ \mathbf{elif}\;y \leq 5.4 \cdot 10^{-22}:\\ \;\;\;\;x \cdot 500\\ \mathbf{else}:\\ \;\;\;\;y \cdot -500\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -2.6e-96)
   (* y -500.0)
   (if (<= y 5.4e-22) (* x 500.0) (* y -500.0))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -2.6e-96) {
		tmp = y * -500.0;
	} else if (y <= 5.4e-22) {
		tmp = x * 500.0;
	} else {
		tmp = y * -500.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-2.6d-96)) then
        tmp = y * (-500.0d0)
    else if (y <= 5.4d-22) then
        tmp = x * 500.0d0
    else
        tmp = y * (-500.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -2.6e-96) {
		tmp = y * -500.0;
	} else if (y <= 5.4e-22) {
		tmp = x * 500.0;
	} else {
		tmp = y * -500.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -2.6e-96:
		tmp = y * -500.0
	elif y <= 5.4e-22:
		tmp = x * 500.0
	else:
		tmp = y * -500.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -2.6e-96)
		tmp = Float64(y * -500.0);
	elseif (y <= 5.4e-22)
		tmp = Float64(x * 500.0);
	else
		tmp = Float64(y * -500.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -2.6e-96)
		tmp = y * -500.0;
	elseif (y <= 5.4e-22)
		tmp = x * 500.0;
	else
		tmp = y * -500.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -2.6e-96], N[(y * -500.0), $MachinePrecision], If[LessEqual[y, 5.4e-22], N[(x * 500.0), $MachinePrecision], N[(y * -500.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -2.6000000000000002e-96 or 5.4000000000000004e-22 < y

   1. Initial program 100.0%

      \[500 \cdot \left(x - y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{-500 \cdot y} \]
   4. Step-by-step derivation

      1. +-rgt-identity N/A

         \[\leadsto \color{blue}{-500 \cdot y + 0} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{y \cdot -500} + 0 \]

      3. accelerator-lowering-fma.f64 77.8

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, -500, 0\right)} \]

   5. Simplified 77.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, -500, 0\right)} \]
   6. Step-by-step derivation

      1. +-rgt-identity N/A

         \[\leadsto \color{blue}{y \cdot -500} \]

      2. *-lowering-*.f64 77.8

         \[\leadsto \color{blue}{y \cdot -500} \]

   7. Applied egg-rr 77.8%

      \[\leadsto \color{blue}{y \cdot -500} \]

   if -2.6000000000000002e-96 < y < 5.4000000000000004e-22

   1. Initial program 100.0%

      \[500 \cdot \left(x - y\right) \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto 500 \cdot \color{blue}{x} \]
   4. Step-by-step derivation

   5. Simplified 85.6%

      \[\leadsto 500 \cdot \color{blue}{x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 80.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.6 \cdot 10^{-96}:\\ \;\;\;\;y \cdot -500\\ \mathbf{elif}\;y \leq 5.4 \cdot 10^{-22}:\\ \;\;\;\;x \cdot 500\\ \mathbf{else}:\\ \;\;\;\;y \cdot -500\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 500 \cdot \left(x - y\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* 500.0 (- x y)))

C

double code(double x, double y) {
	return 500.0 * (x - y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 500.0d0 * (x - y)
end function

Java

public static double code(double x, double y) {
	return 500.0 * (x - y);
}

Python

def code(x, y):
	return 500.0 * (x - y)

Julia

function code(x, y)
	return Float64(500.0 * Float64(x - y))
end

MATLAB

function tmp = code(x, y)
	tmp = 500.0 * (x - y);
end

Wolfram

code[x_, y_] := N[(500.0 * N[(x - y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[500 \cdot \left(x - y\right) \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 4: 50.4% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ x \cdot 500 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x 500.0))

C

double code(double x, double y) {
	return x * 500.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * 500.0d0
end function

Java

public static double code(double x, double y) {
	return x * 500.0;
}

Python

def code(x, y):
	return x * 500.0

Julia

function code(x, y)
	return Float64(x * 500.0)
end

MATLAB

function tmp = code(x, y)
	tmp = x * 500.0;
end

Wolfram

code[x_, y_] := N[(x * 500.0), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[500 \cdot \left(x - y\right) \]
2. Add Preprocessing

3. Taylor expanded in x around inf

   \[\leadsto 500 \cdot \color{blue}{x} \]
4. Step-by-step derivation

5. Simplified 48.6%

   \[\leadsto 500 \cdot \color{blue}{x} \]

6. Final simplification 48.6%

   \[\leadsto x \cdot 500 \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024197 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLABView from colour-2.3.3, B"
  :precision binary64
  (* 500.0 (- x y)))