Linear.V2:$cdot from linear-1.19.1.3, A

Percentage Accurate: 99.1% → 99.6%

Time: 5.6s

Alternatives: 4

Speedup: 1.0×

• 23.0% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x \cdot y + z \cdot t \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot y + z \cdot t \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.6% accurate, 0.1× speedup

Math

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

FPCore

(FPCore (x y z t) :precision binary64 (fma y x (* z t)))

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 99.6%

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

   1. *-commutative N/A

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

   2. fma-define N/A

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

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

      \[\leadsto \mathsf{fma.f64}\left(y, \color{blue}{x}, \left(z \cdot t\right)\right) \]

   4. *-lowering-*.f64 99.6%

      \[\leadsto \mathsf{fma.f64}\left(y, x, \mathsf{*.f64}\left(z, t\right)\right) \]

4. Applied egg-rr 99.6%

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

Alternative 2: 76.2% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \cdot x \leq -2 \cdot 10^{+50}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \cdot x \leq 3.2 \cdot 10^{-42}:\\ \;\;\;\;z \cdot t\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (* y x) -2e+50) (* y x) (if (<= (* y x) 3.2e-42) (* z t) (* y x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y * x) <= -2e+50) {
		tmp = y * x;
	} else if ((y * x) <= 3.2e-42) {
		tmp = z * t;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y * x) <= (-2d+50)) then
        tmp = y * x
    else if ((y * x) <= 3.2d-42) then
        tmp = z * t
    else
        tmp = y * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y * x) <= -2e+50) {
		tmp = y * x;
	} else if ((y * x) <= 3.2e-42) {
		tmp = z * t;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y * x) <= -2e+50:
		tmp = y * x
	elif (y * x) <= 3.2e-42:
		tmp = z * t
	else:
		tmp = y * x
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (Float64(y * x) <= -2e+50)
		tmp = Float64(y * x);
	elseif (Float64(y * x) <= 3.2e-42)
		tmp = Float64(z * t);
	else
		tmp = Float64(y * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y * x) <= -2e+50)
		tmp = y * x;
	elseif ((y * x) <= 3.2e-42)
		tmp = z * t;
	else
		tmp = y * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[(y * x), $MachinePrecision], -2e+50], N[(y * x), $MachinePrecision], If[LessEqual[N[(y * x), $MachinePrecision], 3.2e-42], N[(z * t), $MachinePrecision], N[(y * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x y) < -2.0000000000000002e50 or 3.20000000000000025e-42 < (*.f64 x y)

   1. Initial program 99.1%

      \[x \cdot y + z \cdot t \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. *-lowering-*.f64 76.5%

         \[\leadsto \mathsf{*.f64}\left(x, \color{blue}{y}\right) \]

   5. Simplified 76.5%

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

   if -2.0000000000000002e50 < (*.f64 x y) < 3.20000000000000025e-42

   1. Initial program 100.0%

      \[x \cdot y + z \cdot t \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{t \cdot z} \]
   4. Step-by-step derivation

      1. *-lowering-*.f64 84.2%

         \[\leadsto \mathsf{*.f64}\left(t, \color{blue}{z}\right) \]

   5. Simplified 84.2%

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

4. Final simplification 80.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot x \leq -2 \cdot 10^{+50}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \cdot x \leq 3.2 \cdot 10^{-42}:\\ \;\;\;\;z \cdot t\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ z \cdot t + y \cdot x \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.6%

   \[x \cdot y + z \cdot t \]
2. Add Preprocessing

3. Final simplification 99.6%

   \[\leadsto z \cdot t + y \cdot x \]
4. Add Preprocessing

Alternative 4: 52.1% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ z \cdot t \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (* z t))

C

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

Fortran

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

Java

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

Python

def code(x, y, z, t):
	return z * t

Julia

function code(x, y, z, t)
	return Float64(z * t)
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := N[(z * t), $MachinePrecision]

Derivation

1. Initial program 99.6%

   \[x \cdot y + z \cdot t \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{t \cdot z} \]
4. Step-by-step derivation

   1. *-lowering-*.f64 57.0%

      \[\leadsto \mathsf{*.f64}\left(t, \color{blue}{z}\right) \]

5. Simplified 57.0%

   \[\leadsto \color{blue}{t \cdot z} \]

6. Final simplification 57.0%

   \[\leadsto z \cdot t \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024140 
(FPCore (x y z t)
  :name "Linear.V2:$cdot from linear-1.19.1.3, A"
  :precision binary64
  (+ (* x y) (* z t)))