Data.Histogram.Bin.BinF:$cfromIndex from histogram-fill-0.8.4.1

Percentage Accurate: 100.0% → 100.0%

Time: 5.2s

Alternatives: 7

Speedup: 2.3×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y, x, \mathsf{fma}\left(x, 0.5, z\right)\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (fma y x (fma x 0.5 z)))

C

double code(double x, double y, double z) {
	return fma(y, x, fma(x, 0.5, z));
}

Julia

function code(x, y, z)
	return fma(y, x, fma(x, 0.5, z))
end

Wolfram

code[x_, y_, z_] := N[(y * x + N[(x * 0.5 + z), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

   1. +-commutative N/A

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

   2. associate-+l+ N/A

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

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

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

   4. div-inv N/A

      \[\leadsto \mathsf{fma}\left(y, x, \color{blue}{x \cdot \frac{1}{2}} + z\right) \]

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

      \[\leadsto \mathsf{fma}\left(y, x, \color{blue}{\mathsf{fma}\left(x, \frac{1}{2}, z\right)}\right) \]

   6. metadata-eval 100.0

      \[\leadsto \mathsf{fma}\left(y, x, \mathsf{fma}\left(x, \color{blue}{0.5}, z\right)\right) \]

4. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, x, \mathsf{fma}\left(x, 0.5, z\right)\right)} \]
5. Add Preprocessing

Alternative 2: 57.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot x + \frac{x}{2}\\ \mathbf{if}\;t\_0 \leq -1 \cdot 10^{+280}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;t\_0 \leq -5 \cdot 10^{+211}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{elif}\;t\_0 \leq -2 \cdot 10^{-52}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;t\_0 \leq 10:\\ \;\;\;\;z\\ \mathbf{elif}\;t\_0 \leq 4 \cdot 10^{+306}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ (* y x) (/ x 2.0))))
   (if (<= t_0 -1e+280)
     (* y x)
     (if (<= t_0 -5e+211)
       (* x 0.5)
       (if (<= t_0 -2e-52)
         (* y x)
         (if (<= t_0 10.0) z (if (<= t_0 4e+306) (* x 0.5) (* y x))))))))

C

double code(double x, double y, double z) {
	double t_0 = (y * x) + (x / 2.0);
	double tmp;
	if (t_0 <= -1e+280) {
		tmp = y * x;
	} else if (t_0 <= -5e+211) {
		tmp = x * 0.5;
	} else if (t_0 <= -2e-52) {
		tmp = y * x;
	} else if (t_0 <= 10.0) {
		tmp = z;
	} else if (t_0 <= 4e+306) {
		tmp = x * 0.5;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (y * x) + (x / 2.0d0)
    if (t_0 <= (-1d+280)) then
        tmp = y * x
    else if (t_0 <= (-5d+211)) then
        tmp = x * 0.5d0
    else if (t_0 <= (-2d-52)) then
        tmp = y * x
    else if (t_0 <= 10.0d0) then
        tmp = z
    else if (t_0 <= 4d+306) then
        tmp = x * 0.5d0
    else
        tmp = y * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (y * x) + (x / 2.0);
	double tmp;
	if (t_0 <= -1e+280) {
		tmp = y * x;
	} else if (t_0 <= -5e+211) {
		tmp = x * 0.5;
	} else if (t_0 <= -2e-52) {
		tmp = y * x;
	} else if (t_0 <= 10.0) {
		tmp = z;
	} else if (t_0 <= 4e+306) {
		tmp = x * 0.5;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (y * x) + (x / 2.0)
	tmp = 0
	if t_0 <= -1e+280:
		tmp = y * x
	elif t_0 <= -5e+211:
		tmp = x * 0.5
	elif t_0 <= -2e-52:
		tmp = y * x
	elif t_0 <= 10.0:
		tmp = z
	elif t_0 <= 4e+306:
		tmp = x * 0.5
	else:
		tmp = y * x
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(y * x) + Float64(x / 2.0))
	tmp = 0.0
	if (t_0 <= -1e+280)
		tmp = Float64(y * x);
	elseif (t_0 <= -5e+211)
		tmp = Float64(x * 0.5);
	elseif (t_0 <= -2e-52)
		tmp = Float64(y * x);
	elseif (t_0 <= 10.0)
		tmp = z;
	elseif (t_0 <= 4e+306)
		tmp = Float64(x * 0.5);
	else
		tmp = Float64(y * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (y * x) + (x / 2.0);
	tmp = 0.0;
	if (t_0 <= -1e+280)
		tmp = y * x;
	elseif (t_0 <= -5e+211)
		tmp = x * 0.5;
	elseif (t_0 <= -2e-52)
		tmp = y * x;
	elseif (t_0 <= 10.0)
		tmp = z;
	elseif (t_0 <= 4e+306)
		tmp = x * 0.5;
	else
		tmp = y * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(y * x), $MachinePrecision] + N[(x / 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -1e+280], N[(y * x), $MachinePrecision], If[LessEqual[t$95$0, -5e+211], N[(x * 0.5), $MachinePrecision], If[LessEqual[t$95$0, -2e-52], N[(y * x), $MachinePrecision], If[LessEqual[t$95$0, 10.0], z, If[LessEqual[t$95$0, 4e+306], N[(x * 0.5), $MachinePrecision], N[(y * x), $MachinePrecision]]]]]]]

Derivation

1. Split input into 3 regimes

2. if (+.f64 (/.f64 x #s(literal 2 binary64)) (*.f64 y x)) < -1e280 or -4.9999999999999995e211 < (+.f64 (/.f64 x #s(literal 2 binary64)) (*.f64 y x)) < -2e-52 or 4.00000000000000007e306 < (+.f64 (/.f64 x #s(literal 2 binary64)) (*.f64 y x))

   1. Initial program 100.0%

      \[\left(\frac{x}{2} + y \cdot x\right) + z \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

      1. *-lowering-*.f64 71.0

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

   5. Simplified 71.0%

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

   if -1e280 < (+.f64 (/.f64 x #s(literal 2 binary64)) (*.f64 y x)) < -4.9999999999999995e211 or 10 < (+.f64 (/.f64 x #s(literal 2 binary64)) (*.f64 y x)) < 4.00000000000000007e306

   1. Initial program 100.0%

      \[\left(\frac{x}{2} + y \cdot x\right) + z \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

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

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

      2. +-lowering-+.f64 76.8

         \[\leadsto x \cdot \color{blue}{\left(0.5 + y\right)} \]

   5. Simplified 76.8%

      \[\leadsto \color{blue}{x \cdot \left(0.5 + y\right)} \]

   6. Taylor expanded in y around 0

      \[\leadsto x \cdot \color{blue}{\frac{1}{2}} \]
   7. Step-by-step derivation

   8. Simplified 56.6%

      \[\leadsto x \cdot \color{blue}{0.5} \]

   if -2e-52 < (+.f64 (/.f64 x #s(literal 2 binary64)) (*.f64 y x)) < 10

   1. Initial program 100.0%

      \[\left(\frac{x}{2} + y \cdot x\right) + z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

   5. Simplified 78.0%

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

4. Final simplification 69.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \cdot x + \frac{x}{2} \leq -1 \cdot 10^{+280}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \cdot x + \frac{x}{2} \leq -5 \cdot 10^{+211}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{elif}\;y \cdot x + \frac{x}{2} \leq -2 \cdot 10^{-52}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \cdot x + \frac{x}{2} \leq 10:\\ \;\;\;\;z\\ \mathbf{elif}\;y \cdot x + \frac{x}{2} \leq 4 \cdot 10^{+306}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 98.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -75000000:\\ \;\;\;\;\mathsf{fma}\left(y, x, z\right)\\ \mathbf{elif}\;y \leq 0.5:\\ \;\;\;\;\mathsf{fma}\left(x, 0.5, z\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(y, x, z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -75000000.0) (fma y x z) (if (<= y 0.5) (fma x 0.5 z) (fma y x z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -75000000.0) {
		tmp = fma(y, x, z);
	} else if (y <= 0.5) {
		tmp = fma(x, 0.5, z);
	} else {
		tmp = fma(y, x, z);
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -75000000.0)
		tmp = fma(y, x, z);
	elseif (y <= 0.5)
		tmp = fma(x, 0.5, z);
	else
		tmp = fma(y, x, z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -75000000.0], N[(y * x + z), $MachinePrecision], If[LessEqual[y, 0.5], N[(x * 0.5 + z), $MachinePrecision], N[(y * x + z), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -7.5e7 or 0.5 < y

   1. Initial program 100.0%

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

      1. +-commutative N/A

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

      2. associate-+l+ N/A

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

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

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

      4. div-inv N/A

         \[\leadsto \mathsf{fma}\left(y, x, \color{blue}{x \cdot \frac{1}{2}} + z\right) \]

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

         \[\leadsto \mathsf{fma}\left(y, x, \color{blue}{\mathsf{fma}\left(x, \frac{1}{2}, z\right)}\right) \]

      6. metadata-eval 100.0

         \[\leadsto \mathsf{fma}\left(y, x, \mathsf{fma}\left(x, \color{blue}{0.5}, z\right)\right) \]

   4. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y, x, \mathsf{fma}\left(x, 0.5, z\right)\right)} \]

   5. Taylor expanded in x around 0

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

   7. Simplified 98.6%

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

   if -7.5e7 < y < 0.5

   1. Initial program 100.0%

      \[\left(\frac{x}{2} + y \cdot x\right) + z \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

         \[\leadsto \color{blue}{\frac{1}{2} \cdot x + z} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{x \cdot \frac{1}{2}} + z \]

      3. accelerator-lowering-fma.f64 98.8

         \[\leadsto \color{blue}{\mathsf{fma}\left(x, 0.5, z\right)} \]

   5. Simplified 98.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, 0.5, z\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 83.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3 \cdot 10^{+16}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 1.85 \cdot 10^{+112}:\\ \;\;\;\;\mathsf{fma}\left(x, 0.5, z\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -3e+16) (* y x) (if (<= y 1.85e+112) (fma x 0.5 z) (* y x))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -3e+16) {
		tmp = y * x;
	} else if (y <= 1.85e+112) {
		tmp = fma(x, 0.5, z);
	} else {
		tmp = y * x;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -3e+16)
		tmp = Float64(y * x);
	elseif (y <= 1.85e+112)
		tmp = fma(x, 0.5, z);
	else
		tmp = Float64(y * x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -3e+16], N[(y * x), $MachinePrecision], If[LessEqual[y, 1.85e+112], N[(x * 0.5 + z), $MachinePrecision], N[(y * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -3e16 or 1.85000000000000002e112 < y

   1. Initial program 100.0%

      \[\left(\frac{x}{2} + y \cdot x\right) + z \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

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

      1. *-lowering-*.f64 71.8

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

   5. Simplified 71.8%

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

   if -3e16 < y < 1.85000000000000002e112

   1. Initial program 100.0%

      \[\left(\frac{x}{2} + y \cdot x\right) + z \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

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

      1. +-commutative N/A

         \[\leadsto \color{blue}{\frac{1}{2} \cdot x + z} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{x \cdot \frac{1}{2}} + z \]

      3. accelerator-lowering-fma.f64 93.8

         \[\leadsto \color{blue}{\mathsf{fma}\left(x, 0.5, z\right)} \]

   5. Simplified 93.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, 0.5, z\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 85.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3 \cdot 10^{+16}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 1.85 \cdot 10^{+112}:\\ \;\;\;\;\mathsf{fma}\left(x, 0.5, z\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 49.2% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.2 \cdot 10^{+146}:\\ \;\;\;\;x \cdot 0.5\\ \mathbf{elif}\;x \leq 5.6 \cdot 10^{-56}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;x \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -3.2e+146) (* x 0.5) (if (<= x 5.6e-56) z (* x 0.5))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -3.2e+146) {
		tmp = x * 0.5;
	} else if (x <= 5.6e-56) {
		tmp = z;
	} else {
		tmp = x * 0.5;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-3.2d+146)) then
        tmp = x * 0.5d0
    else if (x <= 5.6d-56) then
        tmp = z
    else
        tmp = x * 0.5d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -3.2e+146) {
		tmp = x * 0.5;
	} else if (x <= 5.6e-56) {
		tmp = z;
	} else {
		tmp = x * 0.5;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -3.2e+146:
		tmp = x * 0.5
	elif x <= 5.6e-56:
		tmp = z
	else:
		tmp = x * 0.5
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -3.2e+146)
		tmp = Float64(x * 0.5);
	elseif (x <= 5.6e-56)
		tmp = z;
	else
		tmp = Float64(x * 0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -3.2e+146)
		tmp = x * 0.5;
	elseif (x <= 5.6e-56)
		tmp = z;
	else
		tmp = x * 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -3.2e+146], N[(x * 0.5), $MachinePrecision], If[LessEqual[x, 5.6e-56], z, N[(x * 0.5), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -3.2e146 or 5.59999999999999986e-56 < x

   1. Initial program 100.0%

      \[\left(\frac{x}{2} + y \cdot x\right) + z \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

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

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

      2. +-lowering-+.f64 85.5

         \[\leadsto x \cdot \color{blue}{\left(0.5 + y\right)} \]

   5. Simplified 85.5%

      \[\leadsto \color{blue}{x \cdot \left(0.5 + y\right)} \]

   6. Taylor expanded in y around 0

      \[\leadsto x \cdot \color{blue}{\frac{1}{2}} \]
   7. Step-by-step derivation

   8. Simplified 47.2%

      \[\leadsto x \cdot \color{blue}{0.5} \]

   if -3.2e146 < x < 5.59999999999999986e-56

   1. Initial program 100.0%

      \[\left(\frac{x}{2} + y \cdot x\right) + z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

   5. Simplified 66.4%

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

Alternative 6: 100.0% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(y + 0.5, x, z\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (fma (+ y 0.5) x z))

C

double code(double x, double y, double z) {
	return fma((y + 0.5), x, z);
}

Julia

function code(x, y, z)
	return fma(Float64(y + 0.5), x, z)
end

Wolfram

code[x_, y_, z_] := N[(N[(y + 0.5), $MachinePrecision] * x + z), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

   1. div-inv N/A

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

   2. *-commutative N/A

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

   3. distribute-lft-out N/A

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

   4. *-commutative N/A

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

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

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{1}{2} + y, x, z\right)} \]

   6. +-commutative N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{y + \frac{1}{2}}, x, z\right) \]

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

      \[\leadsto \mathsf{fma}\left(\color{blue}{y + \frac{1}{2}}, x, z\right) \]

   8. metadata-eval 100.0

      \[\leadsto \mathsf{fma}\left(y + \color{blue}{0.5}, x, z\right) \]

4. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y + 0.5, x, z\right)} \]
5. Add Preprocessing

Alternative 7: 40.2% accurate, 23.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

function code(x, y, z)
	return z
end

MATLAB

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

Wolfram

code[x_, y_, z_] := z

Derivation

1. Initial program 100.0%

   \[\left(\frac{x}{2} + y \cdot x\right) + z \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Simplified 43.4%

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

Reproduce

herbie shell --seed 2024207 
(FPCore (x y z)
  :name "Data.Histogram.Bin.BinF:$cfromIndex from histogram-fill-0.8.4.1"
  :precision binary64
  (+ (+ (/ x 2.0) (* y x)) z))