Numeric.Log:$clog1p from log-domain-0.10.2.1, A

Percentage Accurate: 100.0% → 100.0%

Time: 10.3s

Alternatives: 9

Speedup: 1.5×

• 42.7% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = ((x * 2.0d0) + (x * x)) + (y * y)
end function

Java

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

Python

def code(x, y):
	return ((x * 2.0) + (x * x)) + (y * y)

Julia

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

MATLAB

function tmp = code(x, y)
	tmp = ((x * 2.0) + (x * x)) + (y * y);
end

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = ((x * 2.0d0) + (x * x)) + (y * y)
end function

Java

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

Python

def code(x, y):
	return ((x * 2.0) + (x * x)) + (y * y)

Julia

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

MATLAB

function tmp = code(x, y)
	tmp = ((x * 2.0) + (x * x)) + (y * y);
end

Wolfram

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

Alternative 1: 100.0% accurate, 1.2× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. +-commutative N/A

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

   2. associate-+l+ N/A

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

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

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

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

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

   5. *-lowering-*.f64 100.0

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

4. Applied egg-rr 100.0%

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

Alternative 2: 87.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.3 \cdot 10^{+79}:\\ \;\;\;\;x \cdot x\\ \mathbf{elif}\;x \leq 1.56 \cdot 10^{+128}:\\ \;\;\;\;\mathsf{fma}\left(2, x, y \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -2.3e+79)
   (* x x)
   (if (<= x 1.56e+128) (fma 2.0 x (* y y)) (* x x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -2.3e+79) {
		tmp = x * x;
	} else if (x <= 1.56e+128) {
		tmp = fma(2.0, x, (y * y));
	} else {
		tmp = x * x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -2.3e+79)
		tmp = Float64(x * x);
	elseif (x <= 1.56e+128)
		tmp = fma(2.0, x, Float64(y * y));
	else
		tmp = Float64(x * x);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[x, -2.3e+79], N[(x * x), $MachinePrecision], If[LessEqual[x, 1.56e+128], N[(2.0 * x + N[(y * y), $MachinePrecision]), $MachinePrecision], N[(x * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -2.3e79 or 1.55999999999999992e128 < x

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2}} \]
   4. Step-by-step derivation

      1. +-rgt-identity N/A

         \[\leadsto \color{blue}{{x}^{2} + 0} \]

      2. unpow2 N/A

         \[\leadsto \color{blue}{x \cdot x} + 0 \]

      3. accelerator-lowering-fma.f64 95.7

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

   5. Simplified 95.7%

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

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 95.7

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

   7. Applied egg-rr 95.7%

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

   if -2.3e79 < x < 1.55999999999999992e128

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot x + {y}^{2}} \]
   4. Step-by-step derivation

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

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

      2. +-lft-identity N/A

         \[\leadsto \mathsf{fma}\left(2, x, \color{blue}{0 + {y}^{2}}\right) \]

      3. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(2, x, \color{blue}{{y}^{2} + 0}\right) \]

      4. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(2, x, \color{blue}{y \cdot y} + 0\right) \]

      5. accelerator-lowering-fma.f64 89.2

         \[\leadsto \mathsf{fma}\left(2, x, \color{blue}{\mathsf{fma}\left(y, y, 0\right)}\right) \]

   5. Simplified 89.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(2, x, \mathsf{fma}\left(y, y, 0\right)\right)} \]
   6. Step-by-step derivation

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 89.2

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

   7. Applied egg-rr 89.2%

      \[\leadsto \mathsf{fma}\left(2, x, \color{blue}{y \cdot y}\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 96.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \cdot y \leq 10^{-186}:\\ \;\;\;\;\mathsf{fma}\left(x, x, x \cdot 2\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(x, x, y \cdot y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* y y) 1e-186) (fma x x (* x 2.0)) (fma x x (* y y))))

C

double code(double x, double y) {
	double tmp;
	if ((y * y) <= 1e-186) {
		tmp = fma(x, x, (x * 2.0));
	} else {
		tmp = fma(x, x, (y * y));
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(y * y) <= 1e-186)
		tmp = fma(x, x, Float64(x * 2.0));
	else
		tmp = fma(x, x, Float64(y * y));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(y * y), $MachinePrecision], 1e-186], N[(x * x + N[(x * 2.0), $MachinePrecision]), $MachinePrecision], N[(x * x + N[(y * y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 y y) < 9.9999999999999991e-187

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + 2 \cdot \frac{1}{x}\right)} \]
   4. Step-by-step derivation

      1. unpow2 N/A

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

      2. associate-*l* N/A

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

      3. distribute-rgt-in N/A

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

      4. associate-*l* N/A

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

      5. lft-mult-inverse N/A

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

      6. *-commutative N/A

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

      7. distribute-lft-in N/A

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

      8. +-commutative N/A

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

      9. *-lft-identity N/A

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

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

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

      11. +-lowering-+.f64 96.7

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

   5. Simplified 96.7%

      \[\leadsto \color{blue}{x \cdot \left(2 + x\right)} \]
   6. Step-by-step derivation

      1. distribute-rgt-in N/A

         \[\leadsto \color{blue}{2 \cdot x + x \cdot x} \]

      2. +-commutative N/A

         \[\leadsto \color{blue}{x \cdot x + 2 \cdot x} \]

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

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

      4. +-rgt-identity N/A

         \[\leadsto \mathsf{fma}\left(x, x, \color{blue}{2 \cdot x + 0}\right) \]

      5. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(x, x, \color{blue}{x \cdot 2} + 0\right) \]

      6. accelerator-lowering-fma.f64 96.7

         \[\leadsto \mathsf{fma}\left(x, x, \color{blue}{\mathsf{fma}\left(x, 2, 0\right)}\right) \]

   7. Applied egg-rr 96.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, \mathsf{fma}\left(x, 2, 0\right)\right)} \]
   8. Step-by-step derivation

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 96.7

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

   9. Applied egg-rr 96.7%

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

   if 9.9999999999999991e-187 < (*.f64 y y)

   1. Initial program 100.0%

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

      1. +-commutative N/A

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

      2. associate-+l+ N/A

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

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

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

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

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

      5. *-lowering-*.f64 100.0

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

   4. Applied egg-rr 100.0%

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

   5. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(x, x, \color{blue}{{y}^{2}}\right) \]
   6. Step-by-step derivation

      1. +-lft-identity N/A

         \[\leadsto \mathsf{fma}\left(x, x, \color{blue}{0 + {y}^{2}}\right) \]

      2. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(x, x, \color{blue}{{y}^{2} + 0}\right) \]

      3. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(x, x, \color{blue}{y \cdot y} + 0\right) \]

      4. accelerator-lowering-fma.f64 98.1

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

   7. Simplified 98.1%

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

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 98.1

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

   9. Applied egg-rr 98.1%

      \[\leadsto \mathsf{fma}\left(x, x, \color{blue}{y \cdot y}\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 84.5% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \cdot y \leq 2 \cdot 10^{+23}:\\ \;\;\;\;x \cdot \left(x + 2\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* y y) 2e+23) (* x (+ x 2.0)) (* y y)))

C

double code(double x, double y) {
	double tmp;
	if ((y * y) <= 2e+23) {
		tmp = x * (x + 2.0);
	} else {
		tmp = y * y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y * y) <= 2d+23) then
        tmp = x * (x + 2.0d0)
    else
        tmp = y * y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y * y) <= 2e+23) {
		tmp = x * (x + 2.0);
	} else {
		tmp = y * y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y * y) <= 2e+23:
		tmp = x * (x + 2.0)
	else:
		tmp = y * y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(y * y) <= 2e+23)
		tmp = Float64(x * Float64(x + 2.0));
	else
		tmp = Float64(y * y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y * y) <= 2e+23)
		tmp = x * (x + 2.0);
	else
		tmp = y * y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(y * y), $MachinePrecision], 2e+23], N[(x * N[(x + 2.0), $MachinePrecision]), $MachinePrecision], N[(y * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 y y) < 1.9999999999999998e23

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + 2 \cdot \frac{1}{x}\right)} \]
   4. Step-by-step derivation

      1. unpow2 N/A

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

      2. associate-*l* N/A

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

      3. distribute-rgt-in N/A

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

      4. associate-*l* N/A

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

      5. lft-mult-inverse N/A

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

      6. *-commutative N/A

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

      7. distribute-lft-in N/A

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

      8. +-commutative N/A

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

      9. *-lft-identity N/A

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

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

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

      11. +-lowering-+.f64 86.0

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

   5. Simplified 86.0%

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

   if 1.9999999999999998e23 < (*.f64 y y)

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{{y}^{2}} \]
   4. Step-by-step derivation

      1. +-lft-identity N/A

         \[\leadsto \color{blue}{0 + {y}^{2}} \]

      2. +-commutative N/A

         \[\leadsto \color{blue}{{y}^{2} + 0} \]

      3. unpow2 N/A

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

      4. accelerator-lowering-fma.f64 87.5

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

   5. Simplified 87.5%

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

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 87.5

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

   7. Applied egg-rr 87.5%

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

4. Final simplification 86.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot y \leq 2 \cdot 10^{+23}:\\ \;\;\;\;x \cdot \left(x + 2\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 69.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.2 \cdot 10^{+79}:\\ \;\;\;\;x \cdot x\\ \mathbf{elif}\;x \leq 1.56 \cdot 10^{+128}:\\ \;\;\;\;y \cdot y\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -4.2e+79) (* x x) (if (<= x 1.56e+128) (* y y) (* x x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -4.2e+79) {
		tmp = x * x;
	} else if (x <= 1.56e+128) {
		tmp = y * y;
	} else {
		tmp = x * x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-4.2d+79)) then
        tmp = x * x
    else if (x <= 1.56d+128) then
        tmp = y * y
    else
        tmp = x * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -4.2e+79) {
		tmp = x * x;
	} else if (x <= 1.56e+128) {
		tmp = y * y;
	} else {
		tmp = x * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -4.2e+79:
		tmp = x * x
	elif x <= 1.56e+128:
		tmp = y * y
	else:
		tmp = x * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -4.2e+79)
		tmp = Float64(x * x);
	elseif (x <= 1.56e+128)
		tmp = Float64(y * y);
	else
		tmp = Float64(x * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -4.2e+79)
		tmp = x * x;
	elseif (x <= 1.56e+128)
		tmp = y * y;
	else
		tmp = x * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -4.2e+79], N[(x * x), $MachinePrecision], If[LessEqual[x, 1.56e+128], N[(y * y), $MachinePrecision], N[(x * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -4.20000000000000016e79 or 1.55999999999999992e128 < x

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2}} \]
   4. Step-by-step derivation

      1. +-rgt-identity N/A

         \[\leadsto \color{blue}{{x}^{2} + 0} \]

      2. unpow2 N/A

         \[\leadsto \color{blue}{x \cdot x} + 0 \]

      3. accelerator-lowering-fma.f64 95.7

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

   5. Simplified 95.7%

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

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 95.7

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

   7. Applied egg-rr 95.7%

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

   if -4.20000000000000016e79 < x < 1.55999999999999992e128

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{{y}^{2}} \]
   4. Step-by-step derivation

      1. +-lft-identity N/A

         \[\leadsto \color{blue}{0 + {y}^{2}} \]

      2. +-commutative N/A

         \[\leadsto \color{blue}{{y}^{2} + 0} \]

      3. unpow2 N/A

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

      4. accelerator-lowering-fma.f64 64.5

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

   5. Simplified 64.5%

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

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 64.5

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

   7. Applied egg-rr 64.5%

      \[\leadsto \color{blue}{y \cdot y} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 59.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2:\\ \;\;\;\;x \cdot x\\ \mathbf{elif}\;x \leq 2:\\ \;\;\;\;x \cdot 2\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x -2.0) (* x x) (if (<= x 2.0) (* x 2.0) (* x x))))

C

double code(double x, double y) {
	double tmp;
	if (x <= -2.0) {
		tmp = x * x;
	} else if (x <= 2.0) {
		tmp = x * 2.0;
	} else {
		tmp = x * x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= (-2.0d0)) then
        tmp = x * x
    else if (x <= 2.0d0) then
        tmp = x * 2.0d0
    else
        tmp = x * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= -2.0) {
		tmp = x * x;
	} else if (x <= 2.0) {
		tmp = x * 2.0;
	} else {
		tmp = x * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= -2.0:
		tmp = x * x
	elif x <= 2.0:
		tmp = x * 2.0
	else:
		tmp = x * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= -2.0)
		tmp = Float64(x * x);
	elseif (x <= 2.0)
		tmp = Float64(x * 2.0);
	else
		tmp = Float64(x * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= -2.0)
		tmp = x * x;
	elseif (x <= 2.0)
		tmp = x * 2.0;
	else
		tmp = x * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, -2.0], N[(x * x), $MachinePrecision], If[LessEqual[x, 2.0], N[(x * 2.0), $MachinePrecision], N[(x * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -2 or 2 < x

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2}} \]
   4. Step-by-step derivation

      1. +-rgt-identity N/A

         \[\leadsto \color{blue}{{x}^{2} + 0} \]

      2. unpow2 N/A

         \[\leadsto \color{blue}{x \cdot x} + 0 \]

      3. accelerator-lowering-fma.f64 75.5

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

   5. Simplified 75.5%

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

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 75.5

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

   7. Applied egg-rr 75.5%

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

   if -2 < x < 2

   1. Initial program 100.0%

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

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + 2 \cdot \frac{1}{x}\right)} \]
   4. Step-by-step derivation

      1. unpow2 N/A

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

      2. associate-*l* N/A

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

      3. distribute-rgt-in N/A

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

      4. associate-*l* N/A

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

      5. lft-mult-inverse N/A

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

      6. *-commutative N/A

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

      7. distribute-lft-in N/A

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

      8. +-commutative N/A

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

      9. *-lft-identity N/A

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

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

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

      11. +-lowering-+.f64 36.9

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

   5. Simplified 36.9%

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

   6. Taylor expanded in x around 0

      \[\leadsto x \cdot \color{blue}{2} \]
   7. Step-by-step derivation

   8. Simplified 35.2%

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

Alternative 7: 100.0% accurate, 1.5× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. distribute-lft-out N/A

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

   2. *-commutative N/A

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

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

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

   4. +-commutative N/A

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

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

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

   6. *-lowering-*.f64 100.0

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

4. Applied egg-rr 100.0%

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

Alternative 8: 100.0% accurate, 1.5× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

   1. +-commutative N/A

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

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

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

   3. distribute-lft-out N/A

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

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

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

   5. +-commutative N/A

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

   6. +-lowering-+.f64 100.0

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

4. Applied egg-rr 100.0%

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

Alternative 9: 20.7% accurate, 3.7× speedup

Math

\[\begin{array}{l} \\ x \cdot 2 \end{array} \]

FPCore

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

C

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

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * 2.0d0
end function

Java

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

Python

def code(x, y):
	return x * 2.0

Julia

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

MATLAB

function tmp = code(x, y)
	tmp = x * 2.0;
end

Wolfram

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

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around inf

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + 2 \cdot \frac{1}{x}\right)} \]
4. Step-by-step derivation

   1. unpow2 N/A

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

   2. associate-*l* N/A

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

   3. distribute-rgt-in N/A

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

   4. associate-*l* N/A

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

   5. lft-mult-inverse N/A

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

   6. *-commutative N/A

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

   7. distribute-lft-in N/A

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

   8. +-commutative N/A

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

   9. *-lft-identity N/A

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

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

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

   11. +-lowering-+.f64 56.2

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

5. Simplified 56.2%

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

6. Taylor expanded in x around 0

   \[\leadsto x \cdot \color{blue}{2} \]
7. Step-by-step derivation

8. Simplified 19.6%

   \[\leadsto x \cdot \color{blue}{2} \]
9. Add Preprocessing

Developer Target 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (y * y) + ((2.0d0 * x) + (x * x))
end function

Java

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

Python

def code(x, y):
	return (y * y) + ((2.0 * x) + (x * x))

Julia

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

MATLAB

function tmp = code(x, y)
	tmp = (y * y) + ((2.0 * x) + (x * x));
end

Wolfram

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

Reproduce

herbie shell --seed 2024197 
(FPCore (x y)
  :name "Numeric.Log:$clog1p from log-domain-0.10.2.1, A"
  :precision binary64

  :alt
  (! :herbie-platform default (+ (* y y) (+ (* 2 x) (* x x))))

  (+ (+ (* x 2.0) (* x x)) (* y y)))