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

Percentage Accurate: 100.0% → 100.0%

Time: 4.6s

Alternatives: 5

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, 2.3× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

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

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

   4. lower-+.f64 100.0

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

5. Applied rewrites 100.0%

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

Alternative 2: 84.7% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{2} + y \cdot x\\ \mathbf{if}\;t\_0 \leq -1 \cdot 10^{+134} \lor \neg \left(t\_0 \leq 2 \cdot 10^{+86}\right):\\ \;\;\;\;\left(y - -0.5\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(0.5, x, z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ (/ x 2.0) (* y x))))
   (if (or (<= t_0 -1e+134) (not (<= t_0 2e+86)))
     (* (- y -0.5) x)
     (fma 0.5 x z))))

C

double code(double x, double y, double z) {
	double t_0 = (x / 2.0) + (y * x);
	double tmp;
	if ((t_0 <= -1e+134) || !(t_0 <= 2e+86)) {
		tmp = (y - -0.5) * x;
	} else {
		tmp = fma(0.5, x, z);
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(x / 2.0) + Float64(y * x))
	tmp = 0.0
	if ((t_0 <= -1e+134) || !(t_0 <= 2e+86))
		tmp = Float64(Float64(y - -0.5) * x);
	else
		tmp = fma(0.5, x, z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x / 2.0), $MachinePrecision] + N[(y * x), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$0, -1e+134], N[Not[LessEqual[t$95$0, 2e+86]], $MachinePrecision]], N[(N[(y - -0.5), $MachinePrecision] * x), $MachinePrecision], N[(0.5 * x + z), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (/.f64 x #s(literal 2 binary64)) (*.f64 y x)) < -9.99999999999999921e133 or 2e86 < (+.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 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. lower-fma.f64 38.3

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

   5. Applied rewrites 38.3%

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

   6. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. metadata-eval N/A

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

      5. rgt-mult-inverse N/A

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

      6. associate-*r* N/A

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

      7. *-commutative N/A

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

      8. fp-cancel-sign-sub-inv N/A

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

      9. mul-1-neg N/A

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

      10. *-commutative N/A

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

      11. associate-*r* N/A

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

      12. mul-1-neg N/A

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

      13. distribute-lft-neg-out N/A

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

      14. rgt-mult-inverse N/A

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

      15. metadata-eval N/A

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

      16. metadata-eval N/A

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

      17. lower--.f64 91.4

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

   8. Applied rewrites 91.4%

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

   if -9.99999999999999921e133 < (+.f64 (/.f64 x #s(literal 2 binary64)) (*.f64 y x)) < 2e86

   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. lower-fma.f64 85.0

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

   5. Applied rewrites 85.0%

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

4. Final simplification 87.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x}{2} + y \cdot x \leq -1 \cdot 10^{+134} \lor \neg \left(\frac{x}{2} + y \cdot x \leq 2 \cdot 10^{+86}\right):\\ \;\;\;\;\left(y - -0.5\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(0.5, x, z\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 84.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -360000000000 \lor \neg \left(y \leq 6.2 \cdot 10^{+31}\right):\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(0.5, x, z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -360000000000.0) (not (<= y 6.2e+31))) (* y x) (fma 0.5 x z)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -360000000000.0) || !(y <= 6.2e+31)) {
		tmp = y * x;
	} else {
		tmp = fma(0.5, x, z);
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -360000000000.0) || !(y <= 6.2e+31))
		tmp = Float64(y * x);
	else
		tmp = fma(0.5, x, z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -360000000000.0], N[Not[LessEqual[y, 6.2e+31]], $MachinePrecision]], N[(y * x), $MachinePrecision], N[(0.5 * x + z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -3.6e11 or 6.2000000000000004e31 < y

   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. lower-fma.f64 24.7

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

   5. Applied rewrites 24.7%

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

   6. Taylor expanded in y around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 76.2

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

   8. Applied rewrites 76.2%

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

   if -3.6e11 < y < 6.2000000000000004e31

   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. lower-fma.f64 97.3

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

   5. Applied rewrites 97.3%

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

4. Final simplification 87.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -360000000000 \lor \neg \left(y \leq 6.2 \cdot 10^{+31}\right):\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(0.5, x, z\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 59.8% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -60000000000 \lor \neg \left(y \leq 0.5\right):\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;0.5 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -60000000000.0) (not (<= y 0.5))) (* y x) (* 0.5 x)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -60000000000.0) || !(y <= 0.5)) {
		tmp = y * x;
	} else {
		tmp = 0.5 * 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) :: tmp
    if ((y <= (-60000000000.0d0)) .or. (.not. (y <= 0.5d0))) then
        tmp = y * x
    else
        tmp = 0.5d0 * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -60000000000.0) || !(y <= 0.5)) {
		tmp = y * x;
	} else {
		tmp = 0.5 * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -60000000000.0) or not (y <= 0.5):
		tmp = y * x
	else:
		tmp = 0.5 * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -60000000000.0) || !(y <= 0.5))
		tmp = Float64(y * x);
	else
		tmp = Float64(0.5 * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -60000000000.0) || ~((y <= 0.5)))
		tmp = y * x;
	else
		tmp = 0.5 * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -60000000000.0], N[Not[LessEqual[y, 0.5]], $MachinePrecision]], N[(y * x), $MachinePrecision], N[(0.5 * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -6e10 or 0.5 < y

   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. lower-fma.f64 26.3

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

   5. Applied rewrites 26.3%

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

   6. Taylor expanded in y around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 73.8

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

   8. Applied rewrites 73.8%

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

   if -6e10 < 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. lower-fma.f64 99.2

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

   5. Applied rewrites 99.2%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 53.0%

      \[\leadsto 0.5 \cdot \color{blue}{x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 63.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -60000000000 \lor \neg \left(y \leq 0.5\right):\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;0.5 \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 26.0% accurate, 3.8× speedup

Math

\[\begin{array}{l} \\ 0.5 \cdot x \end{array} \]

FPCore

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

C

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

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 0.5d0 * x
end function

Java

public static double code(double x, double y, double z) {
	return 0.5 * x;
}

Python

def code(x, y, z):
	return 0.5 * x

Julia

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

MATLAB

function tmp = code(x, y, z)
	tmp = 0.5 * x;
end

Wolfram

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

Derivation

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. lower-fma.f64 63.9

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

5. Applied rewrites 63.9%

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

6. Taylor expanded in x around inf

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

8. Applied rewrites 29.1%

   \[\leadsto 0.5 \cdot \color{blue}{x} \]
9. Add Preprocessing

Reproduce

herbie shell --seed 2024326 
(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))