_multiplyComplex, imaginary part

Percentage Accurate: 99.2% → 99.6%

Time: 4.0s

Alternatives: 4

Speedup: 1.2×

• 22.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

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_im) + (x_46_im * y_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 = (x_46re * y_46im) + (x_46im * y_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 (x_46_re * y_46_im) + (x_46_im * y_46_re);
}

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.2% accurate, 1.0× speedup

Math

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

FPCore

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

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_im) + (x_46_im * y_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 = (x_46re * y_46im) + (x_46im * y_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 (x_46_re * y_46_im) + (x_46_im * y_46_re);
}

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.6% accurate, 1.2× speedup

Math

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

FPCore

(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (fma y.re x.im (* x.re 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_im, (x_46_re * 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_im, Float64(x_46_re * y_46_im))
end

Wolfram

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

Derivation

1. Initial program 98.8%

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

   1. lift-*.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. +-commutative N/A

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

   4. lift-*.f64 N/A

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

   5. *-commutative N/A

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

   6. lower-fma.f64 99.6

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

4. Applied rewrites 99.6%

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

Alternative 2: 75.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x.re \cdot y.im \leq -1.12 \cdot 10^{+14}:\\ \;\;\;\;x.re \cdot y.im\\ \mathbf{elif}\;x.re \cdot y.im \leq 3.5 \cdot 10^{-55}:\\ \;\;\;\;y.re \cdot x.im\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot y.im\\ \end{array} \end{array} \]

FPCore

(FPCore (x.re x.im y.re y.im)
 :precision binary64
 (if (<= (* x.re y.im) -1.12e+14)
   (* x.re y.im)
   (if (<= (* x.re y.im) 3.5e-55) (* y.re x.im) (* x.re y.im))))

C

double code(double x_46_re, double x_46_im, double y_46_re, double y_46_im) {
	double tmp;
	if ((x_46_re * y_46_im) <= -1.12e+14) {
		tmp = x_46_re * y_46_im;
	} else if ((x_46_re * y_46_im) <= 3.5e-55) {
		tmp = y_46_re * x_46_im;
	} else {
		tmp = x_46_re * y_46_im;
	}
	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) :: tmp
    if ((x_46re * y_46im) <= (-1.12d+14)) then
        tmp = x_46re * y_46im
    else if ((x_46re * y_46im) <= 3.5d-55) then
        tmp = y_46re * x_46im
    else
        tmp = x_46re * y_46im
    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 tmp;
	if ((x_46_re * y_46_im) <= -1.12e+14) {
		tmp = x_46_re * y_46_im;
	} else if ((x_46_re * y_46_im) <= 3.5e-55) {
		tmp = y_46_re * x_46_im;
	} else {
		tmp = x_46_re * y_46_im;
	}
	return tmp;
}

Python

def code(x_46_re, x_46_im, y_46_re, y_46_im):
	tmp = 0
	if (x_46_re * y_46_im) <= -1.12e+14:
		tmp = x_46_re * y_46_im
	elif (x_46_re * y_46_im) <= 3.5e-55:
		tmp = y_46_re * x_46_im
	else:
		tmp = x_46_re * y_46_im
	return tmp

Julia

function code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = 0.0
	if (Float64(x_46_re * y_46_im) <= -1.12e+14)
		tmp = Float64(x_46_re * y_46_im);
	elseif (Float64(x_46_re * y_46_im) <= 3.5e-55)
		tmp = Float64(y_46_re * x_46_im);
	else
		tmp = Float64(x_46_re * y_46_im);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x_46_re, x_46_im, y_46_re, y_46_im)
	tmp = 0.0;
	if ((x_46_re * y_46_im) <= -1.12e+14)
		tmp = x_46_re * y_46_im;
	elseif ((x_46_re * y_46_im) <= 3.5e-55)
		tmp = y_46_re * x_46_im;
	else
		tmp = x_46_re * y_46_im;
	end
	tmp_2 = tmp;
end

Wolfram

code[x$46$re_, x$46$im_, y$46$re_, y$46$im_] := If[LessEqual[N[(x$46$re * y$46$im), $MachinePrecision], -1.12e+14], N[(x$46$re * y$46$im), $MachinePrecision], If[LessEqual[N[(x$46$re * y$46$im), $MachinePrecision], 3.5e-55], N[(y$46$re * x$46$im), $MachinePrecision], N[(x$46$re * y$46$im), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x.re y.im) < -1.12e14 or 3.50000000000000025e-55 < (*.f64 x.re y.im)

   1. Initial program 97.8%

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

   3. Taylor expanded in x.re around inf

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

      1. lower-*.f64 81.5

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

   5. Applied rewrites 81.5%

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

   if -1.12e14 < (*.f64 x.re y.im) < 3.50000000000000025e-55

   1. Initial program 100.0%

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

   3. Taylor expanded in x.re around 0

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

      1. lower-*.f64 80.8

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

   5. Applied rewrites 80.8%

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

4. Final simplification 81.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x.re \cdot y.im \leq -1.12 \cdot 10^{+14}:\\ \;\;\;\;x.re \cdot y.im\\ \mathbf{elif}\;x.re \cdot y.im \leq 3.5 \cdot 10^{-55}:\\ \;\;\;\;y.re \cdot x.im\\ \mathbf{else}:\\ \;\;\;\;x.re \cdot y.im\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.6% accurate, 1.2× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 98.8%

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

   1. lift-*.f64 N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 99.2

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

4. Applied rewrites 99.2%

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

5. Final simplification 99.2%

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

Alternative 4: 52.1% accurate, 2.3× speedup

Math

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

FPCore

(FPCore (x.re x.im y.re y.im) :precision binary64 (* y.re x.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_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_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_im;
}

Python

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

Julia

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

Wolfram

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

Derivation

1. Initial program 98.8%

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

3. Taylor expanded in x.re around 0

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

   1. lower-*.f64 50.0

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

5. Applied rewrites 50.0%

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

6. Final simplification 50.0%

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

Reproduce

herbie shell --seed 2024219 
(FPCore (x.re x.im y.re y.im)
  :name "_multiplyComplex, imaginary part"
  :precision binary64
  (+ (* x.re y.im) (* x.im y.re)))