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

Percentage Accurate: 100.0% → 100.0%

Time: 4.2s

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: 100.0% 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

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. sub-neg N/A

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

   2. +-commutative N/A

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

   3. distribute-rgt-in N/A

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

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

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

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

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

   6. metadata-eval N/A

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

   7. metadata-eval N/A

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

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

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

   9. metadata-eval N/A

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

   10. *-lowering-*.f64 100.0

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

4. Applied egg-rr 100.0%

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

Alternative 2: 74.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -5.5 \cdot 10^{+86}:\\ \;\;\;\;y \cdot -200\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{+49}:\\ \;\;\;\;x \cdot 200\\ \mathbf{else}:\\ \;\;\;\;y \cdot -200\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -5.5e+86)
   (* y -200.0)
   (if (<= y 1.35e+49) (* x 200.0) (* y -200.0))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -5.5e+86) {
		tmp = y * -200.0;
	} else if (y <= 1.35e+49) {
		tmp = x * 200.0;
	} 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 <= (-5.5d+86)) then
        tmp = y * (-200.0d0)
    else if (y <= 1.35d+49) then
        tmp = x * 200.0d0
    else
        tmp = y * (-200.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -5.5e+86) {
		tmp = y * -200.0;
	} else if (y <= 1.35e+49) {
		tmp = x * 200.0;
	} else {
		tmp = y * -200.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -5.5e+86:
		tmp = y * -200.0
	elif y <= 1.35e+49:
		tmp = x * 200.0
	else:
		tmp = y * -200.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -5.5e+86)
		tmp = Float64(y * -200.0);
	elseif (y <= 1.35e+49)
		tmp = Float64(x * 200.0);
	else
		tmp = Float64(y * -200.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -5.5e+86)
		tmp = y * -200.0;
	elseif (y <= 1.35e+49)
		tmp = x * 200.0;
	else
		tmp = y * -200.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -5.5e+86], N[(y * -200.0), $MachinePrecision], If[LessEqual[y, 1.35e+49], N[(x * 200.0), $MachinePrecision], N[(y * -200.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -5.5000000000000002e86 or 1.35000000000000005e49 < y

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

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

      1. +-rgt-identity N/A

         \[\leadsto \color{blue}{-200 \cdot y + 0} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{y \cdot -200} + 0 \]

      3. accelerator-lowering-fma.f64 79.6

         \[\leadsto \color{blue}{\mathsf{fma}\left(y, -200, 0\right)} \]

   5. Simplified 79.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, -200, 0\right)} \]
   6. Step-by-step derivation

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 79.6

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

   7. Applied egg-rr 79.6%

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

   if -5.5000000000000002e86 < y < 1.35000000000000005e49

   1. Initial program 99.9%

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

   3. Taylor expanded in x around inf

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

   5. Simplified 77.9%

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

4. Final simplification 78.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -5.5 \cdot 10^{+86}:\\ \;\;\;\;y \cdot -200\\ \mathbf{elif}\;y \leq 1.35 \cdot 10^{+49}:\\ \;\;\;\;x \cdot 200\\ \mathbf{else}:\\ \;\;\;\;y \cdot -200\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 100.0% accurate, 0.8× speedup

Math

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

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 100.0%

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

   1. sub-neg N/A

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

   2. distribute-rgt-in N/A

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

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

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

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

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

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

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

   6. metadata-eval N/A

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

   7. metadata-eval N/A

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

   8. *-lowering-*.f64 N/A

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

   9. metadata-eval 100.0

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

4. Applied egg-rr 100.0%

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

Alternative 4: 100.0% 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]

Derivation

1. Initial program 100.0%

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

Alternative 5: 51.2% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ x \cdot 200 \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around inf

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

5. Simplified 55.2%

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

6. Final simplification 55.2%

   \[\leadsto x \cdot 200 \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024195 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLABView from colour-2.3.3, C"
  :precision binary64
  (* 200.0 (- x y)))