math.abs on complex

Percentage Accurate: 54.0% → 100.0%

Time: 5.4s

Alternatives: 4

Speedup: 0.2×

Specification

Math

\[\begin{array}{l} \\ \sqrt{re \cdot re + im \cdot im} \end{array} \]

FPCore

(FPCore modulus (re im) :precision binary64 (sqrt (+ (* re re) (* im im))))

C

double modulus(double re, double im) {
	return sqrt(((re * re) + (im * im)));
}

Fortran

real(8) function modulus(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    modulus = sqrt(((re * re) + (im * im)))
end function

Java

public static double modulus(double re, double im) {
	return Math.sqrt(((re * re) + (im * im)));
}

Python

def modulus(re, im):
	return math.sqrt(((re * re) + (im * im)))

Julia

function modulus(re, im)
	return sqrt(Float64(Float64(re * re) + Float64(im * im)))
end

MATLAB

function tmp = modulus(re, im)
	tmp = sqrt(((re * re) + (im * im)));
end

Wolfram

modulus[re_, im_] := N[Sqrt[N[(N[(re * re), $MachinePrecision] + N[(im * im), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Initial Program: 54.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{re \cdot re + im \cdot im} \end{array} \]

FPCore

(FPCore modulus (re im) :precision binary64 (sqrt (+ (* re re) (* im im))))

C

double modulus(double re, double im) {
	return sqrt(((re * re) + (im * im)));
}

Fortran

real(8) function modulus(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    modulus = sqrt(((re * re) + (im * im)))
end function

Java

public static double modulus(double re, double im) {
	return Math.sqrt(((re * re) + (im * im)));
}

Python

def modulus(re, im):
	return math.sqrt(((re * re) + (im * im)))

Julia

function modulus(re, im)
	return sqrt(Float64(Float64(re * re) + Float64(im * im)))
end

MATLAB

function tmp = modulus(re, im)
	tmp = sqrt(((re * re) + (im * im)));
end

Wolfram

modulus[re_, im_] := N[Sqrt[N[(N[(re * re), $MachinePrecision] + N[(im * im), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Alternative 1: 100.0% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \mathsf{hypot}\left(re, im\right) \end{array} \]

FPCore

(FPCore modulus (re im) :precision binary64 (hypot re im))

C

double modulus(double re, double im) {
	return hypot(re, im);
}

Java

public static double modulus(double re, double im) {
	return Math.hypot(re, im);
}

Python

def modulus(re, im):
	return math.hypot(re, im)

Julia

function modulus(re, im)
	return hypot(re, im)
end

MATLAB

function tmp = modulus(re, im)
	tmp = hypot(re, im);
end

Wolfram

modulus[re_, im_] := N[Sqrt[re ^ 2 + im ^ 2], $MachinePrecision]

Derivation

1. Initial program 52.9%

   \[\sqrt{re \cdot re + im \cdot im} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. accelerator-lowering-hypot.f64 100.0

      \[\leadsto \color{blue}{\mathsf{hypot}\left(re, im\right)} \]

4. Applied egg-rr 100.0%

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

Alternative 2: 26.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\frac{re}{im \cdot 2}, re, im\right) \end{array} \]

FPCore

(FPCore modulus (re im) :precision binary64 (fma (/ re (* im 2.0)) re im))

C

double modulus(double re, double im) {
	return fma((re / (im * 2.0)), re, im);
}

Julia

function modulus(re, im)
	return fma(Float64(re / Float64(im * 2.0)), re, im)
end

Wolfram

modulus[re_, im_] := N[(N[(re / N[(im * 2.0), $MachinePrecision]), $MachinePrecision] * re + im), $MachinePrecision]

Derivation

1. Initial program 52.9%

   \[\sqrt{re \cdot re + im \cdot im} \]
2. Add Preprocessing

3. Taylor expanded in re around 0

   \[\leadsto \color{blue}{im + \frac{1}{2} \cdot \frac{{re}^{2}}{im}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{{re}^{2}}{im} + im} \]

   2. *-lft-identity N/A

      \[\leadsto \frac{1}{2} \cdot \frac{\color{blue}{1 \cdot {re}^{2}}}{im} + im \]

   3. associate-*l/ N/A

      \[\leadsto \frac{1}{2} \cdot \color{blue}{\left(\frac{1}{im} \cdot {re}^{2}\right)} + im \]

   4. associate-*l* N/A

      \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \frac{1}{im}\right) \cdot {re}^{2}} + im \]

   5. unpow2 N/A

      \[\leadsto \left(\frac{1}{2} \cdot \frac{1}{im}\right) \cdot \color{blue}{\left(re \cdot re\right)} + im \]

   6. associate-*r* N/A

      \[\leadsto \color{blue}{\left(\left(\frac{1}{2} \cdot \frac{1}{im}\right) \cdot re\right) \cdot re} + im \]

   7. *-commutative N/A

      \[\leadsto \color{blue}{re \cdot \left(\left(\frac{1}{2} \cdot \frac{1}{im}\right) \cdot re\right)} + im \]

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

      \[\leadsto \color{blue}{\mathsf{fma}\left(re, \left(\frac{1}{2} \cdot \frac{1}{im}\right) \cdot re, im\right)} \]

   9. associate-*r/ N/A

      \[\leadsto \mathsf{fma}\left(re, \color{blue}{\frac{\frac{1}{2} \cdot 1}{im}} \cdot re, im\right) \]

   10. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(re, \frac{\color{blue}{\frac{1}{2}}}{im} \cdot re, im\right) \]

   11. associate-*l/ N/A

       \[\leadsto \mathsf{fma}\left(re, \color{blue}{\frac{\frac{1}{2} \cdot re}{im}}, im\right) \]

   12. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{fma}\left(re, \color{blue}{\frac{\frac{1}{2} \cdot re}{im}}, im\right) \]

   13. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(re, \frac{\color{blue}{re \cdot \frac{1}{2}}}{im}, im\right) \]

   14. *-lowering-*.f64 24.5

       \[\leadsto \mathsf{fma}\left(re, \frac{\color{blue}{re \cdot 0.5}}{im}, im\right) \]

5. Simplified 24.5%

   \[\leadsto \color{blue}{\mathsf{fma}\left(re, \frac{re \cdot 0.5}{im}, im\right)} \]
6. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \color{blue}{\frac{re \cdot \frac{1}{2}}{im} \cdot re} + im \]

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

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{re \cdot \frac{1}{2}}{im}, re, im\right)} \]

   3. associate-/l* N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{re \cdot \frac{\frac{1}{2}}{im}}, re, im\right) \]

   4. clear-num N/A

      \[\leadsto \mathsf{fma}\left(re \cdot \color{blue}{\frac{1}{\frac{im}{\frac{1}{2}}}}, re, im\right) \]

   5. un-div-inv N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{re}{\frac{im}{\frac{1}{2}}}}, re, im\right) \]

   6. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{re}{\frac{im}{\frac{1}{2}}}}, re, im\right) \]

   7. div-inv N/A

      \[\leadsto \mathsf{fma}\left(\frac{re}{\color{blue}{im \cdot \frac{1}{\frac{1}{2}}}}, re, im\right) \]

   8. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left(\frac{re}{im \cdot \color{blue}{2}}, re, im\right) \]

   9. *-lowering-*.f64 24.5

      \[\leadsto \mathsf{fma}\left(\frac{re}{\color{blue}{im \cdot 2}}, re, im\right) \]

7. Applied egg-rr 24.5%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{re}{im \cdot 2}, re, im\right)} \]
8. Add Preprocessing

Alternative 3: 26.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(re, \frac{re \cdot 0.5}{im}, im\right) \end{array} \]

FPCore

(FPCore modulus (re im) :precision binary64 (fma re (/ (* re 0.5) im) im))

C

double modulus(double re, double im) {
	return fma(re, ((re * 0.5) / im), im);
}

Julia

function modulus(re, im)
	return fma(re, Float64(Float64(re * 0.5) / im), im)
end

Wolfram

modulus[re_, im_] := N[(re * N[(N[(re * 0.5), $MachinePrecision] / im), $MachinePrecision] + im), $MachinePrecision]

Derivation

1. Initial program 52.9%

   \[\sqrt{re \cdot re + im \cdot im} \]
2. Add Preprocessing

3. Taylor expanded in re around 0

   \[\leadsto \color{blue}{im + \frac{1}{2} \cdot \frac{{re}^{2}}{im}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{{re}^{2}}{im} + im} \]

   2. *-lft-identity N/A

      \[\leadsto \frac{1}{2} \cdot \frac{\color{blue}{1 \cdot {re}^{2}}}{im} + im \]

   3. associate-*l/ N/A

      \[\leadsto \frac{1}{2} \cdot \color{blue}{\left(\frac{1}{im} \cdot {re}^{2}\right)} + im \]

   4. associate-*l* N/A

      \[\leadsto \color{blue}{\left(\frac{1}{2} \cdot \frac{1}{im}\right) \cdot {re}^{2}} + im \]

   5. unpow2 N/A

      \[\leadsto \left(\frac{1}{2} \cdot \frac{1}{im}\right) \cdot \color{blue}{\left(re \cdot re\right)} + im \]

   6. associate-*r* N/A

      \[\leadsto \color{blue}{\left(\left(\frac{1}{2} \cdot \frac{1}{im}\right) \cdot re\right) \cdot re} + im \]

   7. *-commutative N/A

      \[\leadsto \color{blue}{re \cdot \left(\left(\frac{1}{2} \cdot \frac{1}{im}\right) \cdot re\right)} + im \]

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

      \[\leadsto \color{blue}{\mathsf{fma}\left(re, \left(\frac{1}{2} \cdot \frac{1}{im}\right) \cdot re, im\right)} \]

   9. associate-*r/ N/A

      \[\leadsto \mathsf{fma}\left(re, \color{blue}{\frac{\frac{1}{2} \cdot 1}{im}} \cdot re, im\right) \]

   10. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(re, \frac{\color{blue}{\frac{1}{2}}}{im} \cdot re, im\right) \]

   11. associate-*l/ N/A

       \[\leadsto \mathsf{fma}\left(re, \color{blue}{\frac{\frac{1}{2} \cdot re}{im}}, im\right) \]

   12. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{fma}\left(re, \color{blue}{\frac{\frac{1}{2} \cdot re}{im}}, im\right) \]

   13. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(re, \frac{\color{blue}{re \cdot \frac{1}{2}}}{im}, im\right) \]

   14. *-lowering-*.f64 24.5

       \[\leadsto \mathsf{fma}\left(re, \frac{\color{blue}{re \cdot 0.5}}{im}, im\right) \]

5. Simplified 24.5%

   \[\leadsto \color{blue}{\mathsf{fma}\left(re, \frac{re \cdot 0.5}{im}, im\right)} \]
6. Add Preprocessing

Alternative 4: 26.0% accurate, 24.0× speedup

Math

\[\begin{array}{l} \\ im \end{array} \]

FPCore

(FPCore modulus (re im) :precision binary64 im)

C

double modulus(double re, double im) {
	return im;
}

Fortran

real(8) function modulus(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    modulus = im
end function

Java

public static double modulus(double re, double im) {
	return im;
}

Python

def modulus(re, im):
	return im

Julia

function modulus(re, im)
	return im
end

MATLAB

function tmp = modulus(re, im)
	tmp = im;
end

Wolfram

modulus[re_, im_] := im

Derivation

1. Initial program 52.9%

   \[\sqrt{re \cdot re + im \cdot im} \]
2. Add Preprocessing

3. Taylor expanded in re around 0

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

5. Simplified 24.1%

   \[\leadsto \color{blue}{im} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024196 
(FPCore modulus (re im)
  :name "math.abs on complex"
  :precision binary64
  (sqrt (+ (* re re) (* im im))))