Linear.Quaternion:$c/ from linear-1.19.1.3, E

Percentage Accurate: 99.9% → 99.9%

Time: 19.1s

Alternatives: 6

Speedup: 1.0×

• 43.9% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 0.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y \cdot 2, y, {\left(\mathsf{hypot}\left(x, y\right)\right)}^{2}\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma (* y 2.0) y (pow (hypot x y) 2.0)))

C

double code(double x, double y) {
	return fma((y * 2.0), y, pow(hypot(x, y), 2.0));
}

Julia

function code(x, y)
	return fma(Float64(y * 2.0), y, (hypot(x, y) ^ 2.0))
end

Wolfram

code[x_, y_] := N[(N[(y * 2.0), $MachinePrecision] * y + N[Power[N[Sqrt[x ^ 2 + y ^ 2], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

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

   1. associate-+r+ 99.9%

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

   2. +-commutative 99.9%

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

   3. count-2 99.9%

      \[\leadsto \color{blue}{2 \cdot \left(y \cdot y\right)} + \left(x \cdot x + y \cdot y\right) \]

   4. associate-*r* 99.9%

      \[\leadsto \color{blue}{\left(2 \cdot y\right) \cdot y} + \left(x \cdot x + y \cdot y\right) \]

   5. fma-define 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(2 \cdot y, y, x \cdot x + y \cdot y\right)} \]

   6. *-commutative 100.0%

      \[\leadsto \mathsf{fma}\left(\color{blue}{y \cdot 2}, y, x \cdot x + y \cdot y\right) \]

   7. fma-define 100.0%

      \[\leadsto \mathsf{fma}\left(y \cdot 2, y, \color{blue}{\mathsf{fma}\left(x, x, y \cdot y\right)}\right) \]

   8. add-sqr-sqrt 100.0%

      \[\leadsto \mathsf{fma}\left(y \cdot 2, y, \color{blue}{\sqrt{\mathsf{fma}\left(x, x, y \cdot y\right)} \cdot \sqrt{\mathsf{fma}\left(x, x, y \cdot y\right)}}\right) \]

   9. pow2 100.0%

      \[\leadsto \mathsf{fma}\left(y \cdot 2, y, \color{blue}{{\left(\sqrt{\mathsf{fma}\left(x, x, y \cdot y\right)}\right)}^{2}}\right) \]

   10. fma-define 100.0%

       \[\leadsto \mathsf{fma}\left(y \cdot 2, y, {\left(\sqrt{\color{blue}{x \cdot x + y \cdot y}}\right)}^{2}\right) \]

   11. hypot-define 100.0%

       \[\leadsto \mathsf{fma}\left(y \cdot 2, y, {\color{blue}{\left(\mathsf{hypot}\left(x, y\right)\right)}}^{2}\right) \]

4. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y \cdot 2, y, {\left(\mathsf{hypot}\left(x, y\right)\right)}^{2}\right)} \]
5. Add Preprocessing

Alternative 2: 99.9% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y, y, \mathsf{fma}\left(x, x, 2 \cdot \left(y \cdot y\right)\right)\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma y y (fma x x (* 2.0 (* y y)))))

C

double code(double x, double y) {
	return fma(y, y, fma(x, x, (2.0 * (y * y))));
}

Julia

function code(x, y)
	return fma(y, y, fma(x, x, Float64(2.0 * Float64(y * y))))
end

Wolfram

code[x_, y_] := N[(y * y + N[(x * x + N[(2.0 * N[(y * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]
2. Step-by-step derivation

   1. +-commutative 99.9%

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

   2. sqr-neg 99.9%

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

   3. +-commutative 99.9%

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

   4. sqr-neg 99.9%

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

   5. +-commutative 99.9%

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

   6. fma-define 100.0%

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

   7. sqr-neg 100.0%

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

   8. sqr-neg 100.0%

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

   9. associate-+l+ 100.0%

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

   10. fma-define 100.0%

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

   11. count-2 100.0%

       \[\leadsto \mathsf{fma}\left(y, y, \mathsf{fma}\left(x, x, \color{blue}{2 \cdot \left(y \cdot y\right)}\right)\right) \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, y, \mathsf{fma}\left(x, x, 2 \cdot \left(y \cdot y\right)\right)\right)} \]
4. Add Preprocessing
5. Add Preprocessing

Alternative 3: 99.9% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, x, y \cdot y\right) + y \cdot \left(y \cdot 2\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ (fma x x (* y y)) (* y (* y 2.0))))

C

double code(double x, double y) {
	return fma(x, x, (y * y)) + (y * (y * 2.0));
}

Julia

function code(x, y)
	return Float64(fma(x, x, Float64(y * y)) + Float64(y * Float64(y * 2.0)))
end

Wolfram

code[x_, y_] := N[(N[(x * x + N[(y * y), $MachinePrecision]), $MachinePrecision] + N[(y * N[(y * 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(x \cdot x + y \cdot y\right) + y \cdot y\right) + y \cdot y \]
2. Step-by-step derivation

   1. associate-+l+ 99.9%

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

   2. fma-define 99.9%

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

3. Simplified 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, y \cdot y\right) + \left(y \cdot y + y \cdot y\right)} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. distribute-lft-out 99.9%

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

   2. *-commutative 99.9%

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

   3. count-2 99.9%

      \[\leadsto \mathsf{fma}\left(x, x, y \cdot y\right) + \color{blue}{\left(2 \cdot y\right)} \cdot y \]

   4. *-commutative 99.9%

      \[\leadsto \mathsf{fma}\left(x, x, y \cdot y\right) + \color{blue}{\left(y \cdot 2\right)} \cdot y \]

6. Applied egg-rr 99.9%

   \[\leadsto \mathsf{fma}\left(x, x, y \cdot y\right) + \color{blue}{\left(y \cdot 2\right) \cdot y} \]

7. Final simplification 99.9%

   \[\leadsto \mathsf{fma}\left(x, x, y \cdot y\right) + y \cdot \left(y \cdot 2\right) \]
8. Add Preprocessing

Alternative 4: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

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

3. Final simplification 99.9%

   \[\leadsto y \cdot y + \left(y \cdot y + \left(y \cdot y + x \cdot x\right)\right) \]
4. Add Preprocessing

Alternative 5: 57.5% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ y \cdot \left(y \cdot 3\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* y (* y 3.0)))

C

double code(double x, double y) {
	return y * (y * 3.0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = y * (y * 3.0d0)
end function

Java

public static double code(double x, double y) {
	return y * (y * 3.0);
}

Python

def code(x, y):
	return y * (y * 3.0)

Julia

function code(x, y)
	return Float64(y * Float64(y * 3.0))
end

MATLAB

function tmp = code(x, y)
	tmp = y * (y * 3.0);
end

Wolfram

code[x_, y_] := N[(y * N[(y * 3.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

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

   1. associate-+r+ 99.9%

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

   2. +-commutative 99.9%

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

   3. count-2 99.9%

      \[\leadsto \color{blue}{2 \cdot \left(y \cdot y\right)} + \left(x \cdot x + y \cdot y\right) \]

   4. associate-*r* 99.9%

      \[\leadsto \color{blue}{\left(2 \cdot y\right) \cdot y} + \left(x \cdot x + y \cdot y\right) \]

   5. fma-define 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(2 \cdot y, y, x \cdot x + y \cdot y\right)} \]

   6. *-commutative 100.0%

      \[\leadsto \mathsf{fma}\left(\color{blue}{y \cdot 2}, y, x \cdot x + y \cdot y\right) \]

   7. fma-define 100.0%

      \[\leadsto \mathsf{fma}\left(y \cdot 2, y, \color{blue}{\mathsf{fma}\left(x, x, y \cdot y\right)}\right) \]

   8. add-sqr-sqrt 100.0%

      \[\leadsto \mathsf{fma}\left(y \cdot 2, y, \color{blue}{\sqrt{\mathsf{fma}\left(x, x, y \cdot y\right)} \cdot \sqrt{\mathsf{fma}\left(x, x, y \cdot y\right)}}\right) \]

   9. pow2 100.0%

      \[\leadsto \mathsf{fma}\left(y \cdot 2, y, \color{blue}{{\left(\sqrt{\mathsf{fma}\left(x, x, y \cdot y\right)}\right)}^{2}}\right) \]

   10. fma-define 100.0%

       \[\leadsto \mathsf{fma}\left(y \cdot 2, y, {\left(\sqrt{\color{blue}{x \cdot x + y \cdot y}}\right)}^{2}\right) \]

   11. hypot-define 100.0%

       \[\leadsto \mathsf{fma}\left(y \cdot 2, y, {\color{blue}{\left(\mathsf{hypot}\left(x, y\right)\right)}}^{2}\right) \]

4. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y \cdot 2, y, {\left(\mathsf{hypot}\left(x, y\right)\right)}^{2}\right)} \]

6. Applied egg-rr 99.7%

   \[\leadsto \color{blue}{{\left(\mathsf{hypot}\left(y, \mathsf{hypot}\left(y, \mathsf{hypot}\left(y, x\right)\right)\right)\right)}^{2}} \]

7. Taylor expanded in y around inf 59.5%

   \[\leadsto {\color{blue}{\left(y \cdot \sqrt{3}\right)}}^{2} \]
8. Step-by-step derivation

   1. unpow-prod-down 59.4%

      \[\leadsto \color{blue}{{y}^{2} \cdot {\left(\sqrt{3}\right)}^{2}} \]

   2. pow2 59.4%

      \[\leadsto \color{blue}{\left(y \cdot y\right)} \cdot {\left(\sqrt{3}\right)}^{2} \]

   3. pow2 59.4%

      \[\leadsto \left(y \cdot y\right) \cdot \color{blue}{\left(\sqrt{3} \cdot \sqrt{3}\right)} \]

   4. rem-square-sqrt 59.7%

      \[\leadsto \left(y \cdot y\right) \cdot \color{blue}{3} \]

   5. *-commutative 59.7%

      \[\leadsto \color{blue}{3 \cdot \left(y \cdot y\right)} \]

   6. associate-*l* 59.7%

      \[\leadsto \color{blue}{\left(3 \cdot y\right) \cdot y} \]

   7. metadata-eval 59.7%

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

   8. distribute-lft1-in 59.7%

      \[\leadsto \color{blue}{\left(2 \cdot y + y\right)} \cdot y \]

   9. *-un-lft-identity 59.7%

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

   10. distribute-rgt-out 59.7%

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

   11. metadata-eval 59.7%

       \[\leadsto \left(y \cdot \color{blue}{3}\right) \cdot y \]

9. Applied egg-rr 59.7%

   \[\leadsto \color{blue}{\left(y \cdot 3\right) \cdot y} \]

10. Final simplification 59.7%

    \[\leadsto y \cdot \left(y \cdot 3\right) \]
11. Add Preprocessing

Alternative 6: 8.1% accurate, 15.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 0.0)

C

double code(double x, double y) {
	return 0.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 0.0d0
end function

Java

public static double code(double x, double y) {
	return 0.0;
}

Python

def code(x, y):
	return 0.0

Julia

function code(x, y)
	return 0.0
end

MATLAB

function tmp = code(x, y)
	tmp = 0.0;
end

Wolfram

code[x_, y_] := 0.0

Derivation

1. Initial program 99.9%

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

   1. associate-+r+ 99.9%

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

   2. flip3-+ 19.2%

      \[\leadsto \color{blue}{\frac{{\left(x \cdot x\right)}^{3} + {\left(y \cdot y\right)}^{3}}{\left(x \cdot x\right) \cdot \left(x \cdot x\right) + \left(\left(y \cdot y\right) \cdot \left(y \cdot y\right) - \left(x \cdot x\right) \cdot \left(y \cdot y\right)\right)}} + \left(y \cdot y + y \cdot y\right) \]

   3. flip-+ 0.0%

      \[\leadsto \frac{{\left(x \cdot x\right)}^{3} + {\left(y \cdot y\right)}^{3}}{\left(x \cdot x\right) \cdot \left(x \cdot x\right) + \left(\left(y \cdot y\right) \cdot \left(y \cdot y\right) - \left(x \cdot x\right) \cdot \left(y \cdot y\right)\right)} + \color{blue}{\frac{\left(y \cdot y\right) \cdot \left(y \cdot y\right) - \left(y \cdot y\right) \cdot \left(y \cdot y\right)}{y \cdot y - y \cdot y}} \]

   4. frac-add 0.0%

      \[\leadsto \color{blue}{\frac{\left({\left(x \cdot x\right)}^{3} + {\left(y \cdot y\right)}^{3}\right) \cdot \left(y \cdot y - y \cdot y\right) + \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right) + \left(\left(y \cdot y\right) \cdot \left(y \cdot y\right) - \left(x \cdot x\right) \cdot \left(y \cdot y\right)\right)\right) \cdot \left(\left(y \cdot y\right) \cdot \left(y \cdot y\right) - \left(y \cdot y\right) \cdot \left(y \cdot y\right)\right)}{\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right) + \left(\left(y \cdot y\right) \cdot \left(y \cdot y\right) - \left(x \cdot x\right) \cdot \left(y \cdot y\right)\right)\right) \cdot \left(y \cdot y - y \cdot y\right)}} \]

4. Applied egg-rr 0.0%

   \[\leadsto \color{blue}{\frac{\left({x}^{6} + {y}^{6}\right) \cdot 0 + \left({x}^{4} + \left({y}^{4} - {\left(x \cdot y\right)}^{2}\right)\right) \cdot 0}{\left({x}^{4} + \left({y}^{4} - {\left(x \cdot y\right)}^{2}\right)\right) \cdot 0}} \]

6. Simplified 11.6%

   \[\leadsto \color{blue}{0} \]
7. Add Preprocessing

Developer Target 1: 99.9% accurate, 1.4× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024143 
(FPCore (x y)
  :name "Linear.Quaternion:$c/ from linear-1.19.1.3, E"
  :precision binary64

  :alt
  (! :herbie-platform default (+ (* x x) (* y (+ y (+ y y)))))

  (+ (+ (+ (* x x) (* y y)) (* y y)) (* y y)))