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

Percentage Accurate: 100.0% → 100.0%

Time: 9.6s

Alternatives: 7

Speedup: 1.5×

• 43.4% 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, 1.5× 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 2: 60.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(x \cdot 2 + x \cdot x\right) + y \cdot y \leq 2 \cdot 10^{-6}:\\ \;\;\;\;x \cdot 2\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (+ (+ (* x 2.0) (* x x)) (* y y)) 2e-6) (* x 2.0) (* x x)))

C

double code(double x, double y) {
	double tmp;
	if ((((x * 2.0) + (x * x)) + (y * y)) <= 2e-6) {
		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) + (x * x)) + (y * y)) <= 2d-6) 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) + (x * x)) + (y * y)) <= 2e-6) {
		tmp = x * 2.0;
	} else {
		tmp = x * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (((x * 2.0) + (x * x)) + (y * y)) <= 2e-6:
		tmp = x * 2.0
	else:
		tmp = x * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(Float64(x * 2.0) + Float64(x * x)) + Float64(y * y)) <= 2e-6)
		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) + (x * x)) + (y * y)) <= 2e-6)
		tmp = x * 2.0;
	else
		tmp = x * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(N[(x * 2.0), $MachinePrecision] + N[(x * x), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision], 2e-6], N[(x * 2.0), $MachinePrecision], N[(x * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (+.f64 (*.f64 x #s(literal 2 binary64)) (*.f64 x x)) (*.f64 y y)) < 1.99999999999999991e-6

   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 72.7

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

   5. Simplified 72.7%

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

   6. Taylor expanded in x around 0

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

   8. Simplified 72.3%

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

   if 1.99999999999999991e-6 < (+.f64 (+.f64 (*.f64 x #s(literal 2 binary64)) (*.f64 x x)) (*.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 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 55.4

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

   5. Simplified 55.4%

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

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 55.4

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

   7. Applied egg-rr 55.4%

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

Alternative 3: 98.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot 2 + x \cdot x \leq 2 \cdot 10^{-6}:\\ \;\;\;\;\mathsf{fma}\left(y, y, x \cdot 2\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(y, y, x \cdot x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (+ (* x 2.0) (* x x)) 2e-6) (fma y y (* x 2.0)) (fma y y (* x x))))

C

double code(double x, double y) {
	double tmp;
	if (((x * 2.0) + (x * x)) <= 2e-6) {
		tmp = fma(y, y, (x * 2.0));
	} else {
		tmp = fma(y, y, (x * x));
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(x * 2.0) + Float64(x * x)) <= 2e-6)
		tmp = fma(y, y, Float64(x * 2.0));
	else
		tmp = fma(y, y, Float64(x * x));
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(x * 2.0), $MachinePrecision] + N[(x * x), $MachinePrecision]), $MachinePrecision], 2e-6], N[(y * y + N[(x * 2.0), $MachinePrecision]), $MachinePrecision], N[(y * y + N[(x * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (*.f64 x #s(literal 2 binary64)) (*.f64 x x)) < 1.99999999999999991e-6

   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 99.4

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

   5. Simplified 99.4%

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

      1. +-rgt-identity N/A

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

      2. +-commutative N/A

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

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

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

      4. *-commutative N/A

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

      5. *-lowering-*.f64 99.4

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

   7. Applied egg-rr 99.4%

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

   if 1.99999999999999991e-6 < (+.f64 (*.f64 x #s(literal 2 binary64)) (*.f64 x x))

   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. Taylor expanded in x around inf

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

   7. Simplified 99.5%

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

Alternative 4: 89.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot 2 + x \cdot x \leq 10^{+127}:\\ \;\;\;\;\mathsf{fma}\left(y, y, x \cdot 2\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (+ (* x 2.0) (* x x)) 1e+127) (fma y y (* x 2.0)) (* x x)))

C

double code(double x, double y) {
	double tmp;
	if (((x * 2.0) + (x * x)) <= 1e+127) {
		tmp = fma(y, y, (x * 2.0));
	} else {
		tmp = x * x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(x * 2.0) + Float64(x * x)) <= 1e+127)
		tmp = fma(y, y, Float64(x * 2.0));
	else
		tmp = Float64(x * x);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(x * 2.0), $MachinePrecision] + N[(x * x), $MachinePrecision]), $MachinePrecision], 1e+127], N[(y * y + N[(x * 2.0), $MachinePrecision]), $MachinePrecision], N[(x * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (*.f64 x #s(literal 2 binary64)) (*.f64 x x)) < 9.99999999999999955e126

   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 94.6

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

   5. Simplified 94.6%

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

      1. +-rgt-identity N/A

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

      2. +-commutative N/A

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

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

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

      4. *-commutative N/A

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

      5. *-lowering-*.f64 94.6

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

   7. Applied egg-rr 94.6%

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

   if 9.99999999999999955e126 < (+.f64 (*.f64 x #s(literal 2 binary64)) (*.f64 x 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 94.0

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

   5. Simplified 94.0%

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

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 94.0

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

   7. Applied egg-rr 94.0%

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

Alternative 5: 71.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot 2 + x \cdot x \leq 10^{+127}:\\ \;\;\;\;y \cdot y\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (+ (* x 2.0) (* x x)) 1e+127) (* y y) (* x x)))

C

double code(double x, double y) {
	double tmp;
	if (((x * 2.0) + (x * x)) <= 1e+127) {
		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 * 2.0d0) + (x * x)) <= 1d+127) 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 * 2.0) + (x * x)) <= 1e+127) {
		tmp = y * y;
	} else {
		tmp = x * x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if ((x * 2.0) + (x * x)) <= 1e+127:
		tmp = y * y
	else:
		tmp = x * x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(Float64(x * 2.0) + Float64(x * x)) <= 1e+127)
		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 * 2.0) + (x * x)) <= 1e+127)
		tmp = y * y;
	else
		tmp = x * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[(x * 2.0), $MachinePrecision] + N[(x * x), $MachinePrecision]), $MachinePrecision], 1e+127], N[(y * y), $MachinePrecision], N[(x * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (*.f64 x #s(literal 2 binary64)) (*.f64 x x)) < 9.99999999999999955e126

   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 60.5

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

   5. Simplified 60.5%

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

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 60.5

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

   7. Applied egg-rr 60.5%

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

   if 9.99999999999999955e126 < (+.f64 (*.f64 x #s(literal 2 binary64)) (*.f64 x 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 94.0

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

   5. Simplified 94.0%

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

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 94.0

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

   7. Applied egg-rr 94.0%

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

Alternative 6: 84.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \cdot y \leq 2 \cdot 10^{+62}:\\ \;\;\;\;x \cdot \left(x + 2\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* y y) 2e+62) (* x (+ x 2.0)) (* y y)))

C

double code(double x, double y) {
	double tmp;
	if ((y * y) <= 2e+62) {
		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+62) 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+62) {
		tmp = x * (x + 2.0);
	} else {
		tmp = y * y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y * y) <= 2e+62:
		tmp = x * (x + 2.0)
	else:
		tmp = y * y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(y * y) <= 2e+62)
		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+62)
		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+62], N[(x * N[(x + 2.0), $MachinePrecision]), $MachinePrecision], N[(y * y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 y y) < 2.00000000000000007e62

   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 82.9

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

   5. Simplified 82.9%

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

   if 2.00000000000000007e62 < (*.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 82.9

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

   5. Simplified 82.9%

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

      1. +-rgt-identity N/A

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

      2. *-lowering-*.f64 82.9

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

   7. Applied egg-rr 82.9%

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

4. Final simplification 82.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot y \leq 2 \cdot 10^{+62}:\\ \;\;\;\;x \cdot \left(x + 2\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 21.2% 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 61.0

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

5. Simplified 61.0%

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

6. Taylor expanded in x around 0

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

8. Simplified 24.8%

   \[\leadsto x \cdot \color{blue}{2} \]
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 2024196 
(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)))