_multiplyComplex, real part

Percentage Accurate: 99.2% → 99.6%

Time: 4.0s

Alternatives: 4

Speedup: 1.0×

• 23.9% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x.re \cdot y.re - x.im \cdot y.im \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (- (* x.re y.re) (* x.im y.im)))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return (x_46_re * y_46_re) - (x_46_im * y_46_im);
}

Fortran

real(8) function code(x_46re, x_46im, y_46re, y_46im)
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    code = (x_46re * y_46re) - (x_46im * y_46im)
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return (x_46_re * y_46_re) - (x_46_im * y_46_im);
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	return (x_46_re * y_46_re) - (x_46_im * y_46_im)

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	return Float64(Float64(x_46_re * y_46_re) - Float64(x_46_im * y_46_im))
end

MATLAB

function tmp = code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = (x_46_re * y_46_re) - (x_46_im * y_46_im);
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := N[(N[(x$46$re * y$46$re), $MachinePrecision] - N[(x$46$im * y$46$im), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x.re \cdot y.re - x.im \cdot y.im \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (- (* x.re y.re) (* x.im y.im)))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return (x_46_re * y_46_re) - (x_46_im * y_46_im);
}

Fortran

real(8) function code(x_46re, x_46im, y_46re, y_46im)
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    code = (x_46re * y_46re) - (x_46im * y_46im)
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return (x_46_re * y_46_re) - (x_46_im * y_46_im);
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	return (x_46_re * y_46_re) - (x_46_im * y_46_im)

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	return Float64(Float64(x_46_re * y_46_re) - Float64(x_46_im * y_46_im))
end

MATLAB

function tmp = code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = (x_46_re * y_46_re) - (x_46_im * y_46_im);
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := N[(N[(x$46$re * y$46$re), $MachinePrecision] - N[(x$46$im * y$46$im), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.6% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y.re, x.re, x.im \cdot \left(0 - y.im\right)\right) \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (fma y.re x.re (* x.im (- 0.0 y.im))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return fma(y_46_re, x_46_re, (x_46_im * (0.0 - y_46_im)));
}

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	return fma(y_46_re, x_46_re, Float64(x_46_im * Float64(0.0 - y_46_im)))
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := N[(y$46$re * x$46$re + N[(x$46$im * N[(0.0 - y$46$im), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x.re \cdot y.re - x.im \cdot y.im \]
2. Add Preprocessing
3. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto y.re \cdot x.re - \color{blue}{x.im} \cdot y.im \]

   2. fmm-def N/A

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

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

      \[\leadsto \mathsf{fma.f64}\left(y.re, \color{blue}{x.re}, \left(\mathsf{neg}\left(x.im \cdot y.im\right)\right)\right) \]

   4. neg-sub0 N/A

      \[\leadsto \mathsf{fma.f64}\left(y.re, x.re, \left(0 - x.im \cdot y.im\right)\right) \]

   5. --lowering--.f64 N/A

      \[\leadsto \mathsf{fma.f64}\left(y.re, x.re, \mathsf{\_.f64}\left(0, \left(x.im \cdot y.im\right)\right)\right) \]

   6. *-lowering-*.f64 100.0%

      \[\leadsto \mathsf{fma.f64}\left(y.re, x.re, \mathsf{\_.f64}\left(0, \mathsf{*.f64}\left(x.im, y.im\right)\right)\right) \]

4. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y.re, x.re, 0 - x.im \cdot y.im\right)} \]
5. Step-by-step derivation

   1. sub0-neg N/A

      \[\leadsto \mathsf{fma.f64}\left(y.re, x.re, \left(\mathsf{neg}\left(x.im \cdot y.im\right)\right)\right) \]

   2. neg-lowering-neg.f64 N/A

      \[\leadsto \mathsf{fma.f64}\left(y.re, x.re, \mathsf{neg.f64}\left(\left(x.im \cdot y.im\right)\right)\right) \]

   3. *-lowering-*.f64 100.0%

      \[\leadsto \mathsf{fma.f64}\left(y.re, x.re, \mathsf{neg.f64}\left(\mathsf{*.f64}\left(x.im, y.im\right)\right)\right) \]

6. Applied egg-rr 100.0%

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

7. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(y.re, x.re, x.im \cdot \left(0 - y.im\right)\right) \]
8. Add Preprocessing

Alternative 2: 76.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x.im \cdot \left(0 - y.im\right)\\ \mathbf{if}\;x.im \cdot y.im \leq -5 \cdot 10^{+21}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x.im \cdot y.im \leq 1.2 \cdot 10^{-32}:\\ \;\;\;\;y.re \cdot x.re\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (let* ((t_0 (* x.im (- 0.0 y.im))))
   (if (<= (* x.im y.im) -5e+21)
     t_0
     (if (<= (* x.im y.im) 1.2e-32) (* y.re x.re) t_0))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = x_46_im * (0.0 - y_46_im);
	double tmp;
	if ((x_46_im * y_46_im) <= -5e+21) {
		tmp = t_0;
	} else if ((x_46_im * y_46_im) <= 1.2e-32) {
		tmp = y_46_re * x_46_re;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x_46re, x_46im, y_46re, y_46im)
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x_46im * (0.0d0 - y_46im)
    if ((x_46im * y_46im) <= (-5d+21)) then
        tmp = t_0
    else if ((x_46im * y_46im) <= 1.2d-32) then
        tmp = y_46re * x_46re
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double t_0 = x_46_im * (0.0 - y_46_im);
	double tmp;
	if ((x_46_im * y_46_im) <= -5e+21) {
		tmp = t_0;
	} else if ((x_46_im * y_46_im) <= 1.2e-32) {
		tmp = y_46_re * x_46_re;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	t_0 = x_46_im * (0.0 - y_46_im)
	tmp = 0
	if (x_46_im * y_46_im) <= -5e+21:
		tmp = t_0
	elif (x_46_im * y_46_im) <= 1.2e-32:
		tmp = y_46_re * x_46_re
	else:
		tmp = t_0
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = Float64(x_46_im * Float64(0.0 - y_46_im))
	tmp = 0.0
	if (Float64(x_46_im * y_46_im) <= -5e+21)
		tmp = t_0;
	elseif (Float64(x_46_im * y_46_im) <= 1.2e-32)
		tmp = Float64(y_46_re * x_46_re);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x_46_re, x_46_im, y_46_re, y_46_im)
	t_0 = x_46_im * (0.0 - y_46_im);
	tmp = 0.0;
	if ((x_46_im * y_46_im) <= -5e+21)
		tmp = t_0;
	elseif ((x_46_im * y_46_im) <= 1.2e-32)
		tmp = y_46_re * x_46_re;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := Block[{t$95$0 = N[(x$46$im * N[(0.0 - y$46$im), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(x$46$im * y$46$im), $MachinePrecision], -5e+21], t$95$0, If[LessEqual[N[(x$46$im * y$46$im), $MachinePrecision], 1.2e-32], N[(y$46$re * x$46$re), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x.im y.im) < -5e21 or 1.2000000000000001e-32 < (*.f64 x.im y.im)

   1. Initial program 100.0%

      \[x.re \cdot y.re - x.im \cdot y.im \]
   2. Add Preprocessing

   3. Taylor expanded in x.re around 0

      \[\leadsto \color{blue}{-1 \cdot \left(x.im \cdot y.im\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \mathsf{neg}\left(x.im \cdot y.im\right) \]

      2. neg-sub0 N/A

         \[\leadsto 0 - \color{blue}{x.im \cdot y.im} \]

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

         \[\leadsto \mathsf{\_.f64}\left(0, \color{blue}{\left(x.im \cdot y.im\right)}\right) \]

      4. *-lowering-*.f64 83.7%

         \[\leadsto \mathsf{\_.f64}\left(0, \mathsf{*.f64}\left(x.im, \color{blue}{y.im}\right)\right) \]

   5. Simplified 83.7%

      \[\leadsto \color{blue}{0 - x.im \cdot y.im} \]
   6. Step-by-step derivation

      1. sub0-neg N/A

         \[\leadsto \mathsf{neg}\left(x.im \cdot y.im\right) \]

      2. neg-lowering-neg.f64 N/A

         \[\leadsto \mathsf{neg.f64}\left(\left(x.im \cdot y.im\right)\right) \]

      3. *-lowering-*.f64 83.7%

         \[\leadsto \mathsf{neg.f64}\left(\mathsf{*.f64}\left(x.im, y.im\right)\right) \]

   7. Applied egg-rr 83.7%

      \[\leadsto \color{blue}{-x.im \cdot y.im} \]

   if -5e21 < (*.f64 x.im y.im) < 1.2000000000000001e-32

   1. Initial program 100.0%

      \[x.re \cdot y.re - x.im \cdot y.im \]
   2. Add Preprocessing

   3. Taylor expanded in x.re around inf

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

      1. *-lowering-*.f64 81.0%

         \[\leadsto \mathsf{*.f64}\left(x.re, \color{blue}{y.re}\right) \]

   5. Simplified 81.0%

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

4. Final simplification 82.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x.im \cdot y.im \leq -5 \cdot 10^{+21}:\\ \;\;\;\;x.im \cdot \left(0 - y.im\right)\\ \mathbf{elif}\;x.im \cdot y.im \leq 1.2 \cdot 10^{-32}:\\ \;\;\;\;y.re \cdot x.re\\ \mathbf{else}:\\ \;\;\;\;x.im \cdot \left(0 - y.im\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ y.re \cdot x.re - x.im \cdot y.im \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (- (* y.re x.re) (* x.im y.im)))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return (y_46_re * x_46_re) - (x_46_im * y_46_im);
}

Fortran

real(8) function code(x_46re, x_46im, y_46re, y_46im)
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    code = (y_46re * x_46re) - (x_46im * y_46im)
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return (y_46_re * x_46_re) - (x_46_im * y_46_im);
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	return (y_46_re * x_46_re) - (x_46_im * y_46_im)

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	return Float64(Float64(y_46_re * x_46_re) - Float64(x_46_im * y_46_im))
end

MATLAB

function tmp = code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = (y_46_re * x_46_re) - (x_46_im * y_46_im);
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := N[(N[(y$46$re * x$46$re), $MachinePrecision] - N[(x$46$im * y$46$im), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x.re \cdot y.re - x.im \cdot y.im \]
2. Add Preprocessing

3. Final simplification 100.0%

   \[\leadsto y.re \cdot x.re - x.im \cdot y.im \]
4. Add Preprocessing

Alternative 4: 52.1% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ y.re \cdot x.re \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im) :precision binary64 (* y.re x.re))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return y_46_re * x_46_re;
}

Fortran

real(8) function code(x_46re, x_46im, y_46re, y_46im)
    real(8), intent (in) :: x_46re
    real(8), intent (in) :: x_46im
    real(8), intent (in) :: y_46re
    real(8), intent (in) :: y_46im
    code = y_46re * x_46re
end function

Java

public static double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	return y_46_re * x_46_re;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	return y_46_re * x_46_re

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	return Float64(y_46_re * x_46_re)
end

MATLAB

function tmp = code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = y_46_re * x_46_re;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := N[(y$46$re * x$46$re), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x.re \cdot y.re - x.im \cdot y.im \]
2. Add Preprocessing

3. Taylor expanded in x.re around inf

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

   1. *-lowering-*.f64 49.4%

      \[\leadsto \mathsf{*.f64}\left(x.re, \color{blue}{y.re}\right) \]

5. Simplified 49.4%

   \[\leadsto \color{blue}{x.re \cdot y.re} \]

6. Final simplification 49.4%

   \[\leadsto y.re \cdot x.re \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024160 
(FPCore (x.re x.im y.re y.im)
  :name "_multiplyComplex, real part"
  :precision binary64
  (- (* x.re y.re) (* x.im y.im)))