Graphics.Rendering.Chart.Axis.Types:hBufferRect from Chart-1.5.3

Percentage Accurate: 99.9% → 100.0%

Time: 4.2s

Alternatives: 4

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ x + \frac{x - y}{2} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ (- x y) 2.0)))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{x - y}{2} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ (- x y) 2.0)))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, 1.5, y \cdot -0.5\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma x 1.5 (* y -0.5)))

C

double code(double x, double y) {
	return fma(x, 1.5, (y * -0.5));
}

Julia

function code(x, y)
	return fma(x, 1.5, Float64(y * -0.5))
end

Wolfram

code[x_, y_] := N[(x * 1.5 + N[(y * -0.5), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[x + \frac{x - y}{2} \]
2. Step-by-step derivation

   1. div-sub 99.9%

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

   2. associate-+r- 99.9%

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

   3. sub-neg 99.9%

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

   4. +-commutative 99.9%

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

   5. distribute-neg-frac 99.9%

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

   6. neg-mul-1 99.9%

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

   7. associate-/l* 99.7%

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

   8. associate-/r/ 99.9%

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

   9. *-commutative 99.9%

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

   10. fma-def 99.9%

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

   11. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(y, \color{blue}{-0.5}, x + \frac{x}{2}\right) \]

   12. remove-double-neg 99.9%

       \[\leadsto \mathsf{fma}\left(y, -0.5, \color{blue}{\left(-\left(-x\right)\right)} + \frac{x}{2}\right) \]

   13. neg-mul-1 99.9%

       \[\leadsto \mathsf{fma}\left(y, -0.5, \color{blue}{-1 \cdot \left(-x\right)} + \frac{x}{2}\right) \]

   14. remove-double-neg 99.9%

       \[\leadsto \mathsf{fma}\left(y, -0.5, -1 \cdot \left(-x\right) + \frac{\color{blue}{-\left(-x\right)}}{2}\right) \]

   15. neg-mul-1 99.9%

       \[\leadsto \mathsf{fma}\left(y, -0.5, -1 \cdot \left(-x\right) + \frac{\color{blue}{-1 \cdot \left(-x\right)}}{2}\right) \]

   16. associate-/l* 99.9%

       \[\leadsto \mathsf{fma}\left(y, -0.5, -1 \cdot \left(-x\right) + \color{blue}{\frac{-1}{\frac{2}{-x}}}\right) \]

   17. associate-/r/ 99.9%

       \[\leadsto \mathsf{fma}\left(y, -0.5, -1 \cdot \left(-x\right) + \color{blue}{\frac{-1}{2} \cdot \left(-x\right)}\right) \]

   18. distribute-rgt-out 99.9%

       \[\leadsto \mathsf{fma}\left(y, -0.5, \color{blue}{\left(-x\right) \cdot \left(-1 + \frac{-1}{2}\right)}\right) \]

   19. distribute-lft-neg-in 99.9%

       \[\leadsto \mathsf{fma}\left(y, -0.5, \color{blue}{-x \cdot \left(-1 + \frac{-1}{2}\right)}\right) \]

   20. distribute-rgt-neg-in 99.9%

       \[\leadsto \mathsf{fma}\left(y, -0.5, \color{blue}{x \cdot \left(-\left(-1 + \frac{-1}{2}\right)\right)}\right) \]

   21. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(y, -0.5, x \cdot \left(-\left(-1 + \color{blue}{-0.5}\right)\right)\right) \]

   22. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(y, -0.5, x \cdot \left(-\color{blue}{-1.5}\right)\right) \]

   23. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(y, -0.5, x \cdot \color{blue}{1.5}\right) \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, -0.5, x \cdot 1.5\right)} \]
4. Step-by-step derivation

   1. fma-udef 99.9%

      \[\leadsto \color{blue}{y \cdot -0.5 + x \cdot 1.5} \]

5. Applied egg-rr 99.9%

   \[\leadsto \color{blue}{y \cdot -0.5 + x \cdot 1.5} \]
6. Step-by-step derivation

   1. +-commutative 99.9%

      \[\leadsto \color{blue}{x \cdot 1.5 + y \cdot -0.5} \]

   2. fma-def 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, 1.5, y \cdot -0.5\right)} \]

7. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, 1.5, y \cdot -0.5\right)} \]

8. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(x, 1.5, y \cdot -0.5\right) \]

Alternative 2: 74.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.3 \cdot 10^{+26}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 7.2 \cdot 10^{-85}:\\ \;\;\;\;x \cdot 1.5\\ \mathbf{else}:\\ \;\;\;\;y \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -2.3e+26) (* y -0.5) (if (<= y 7.2e-85) (* x 1.5) (* y -0.5))))

C

double code(double x, double y) {
	double tmp;
	if (y <= -2.3e+26) {
		tmp = y * -0.5;
	} else if (y <= 7.2e-85) {
		tmp = x * 1.5;
	} else {
		tmp = y * -0.5;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-2.3d+26)) then
        tmp = y * (-0.5d0)
    else if (y <= 7.2d-85) then
        tmp = x * 1.5d0
    else
        tmp = y * (-0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -2.3e+26) {
		tmp = y * -0.5;
	} else if (y <= 7.2e-85) {
		tmp = x * 1.5;
	} else {
		tmp = y * -0.5;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -2.3e+26:
		tmp = y * -0.5
	elif y <= 7.2e-85:
		tmp = x * 1.5
	else:
		tmp = y * -0.5
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -2.3e+26)
		tmp = Float64(y * -0.5);
	elseif (y <= 7.2e-85)
		tmp = Float64(x * 1.5);
	else
		tmp = Float64(y * -0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -2.3e+26)
		tmp = y * -0.5;
	elseif (y <= 7.2e-85)
		tmp = x * 1.5;
	else
		tmp = y * -0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -2.3e+26], N[(y * -0.5), $MachinePrecision], If[LessEqual[y, 7.2e-85], N[(x * 1.5), $MachinePrecision], N[(y * -0.5), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -2.3000000000000001e26 or 7.1999999999999996e-85 < y

   1. Initial program 99.9%

      \[x + \frac{x - y}{2} \]

   2. Taylor expanded in x around 0 73.7%

      \[\leadsto \color{blue}{-0.5 \cdot y} \]

   if -2.3000000000000001e26 < y < 7.1999999999999996e-85

   1. Initial program 99.8%

      \[x + \frac{x - y}{2} \]

   2. Taylor expanded in x around inf 78.7%

      \[\leadsto \color{blue}{1.5 \cdot x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 75.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -2.3 \cdot 10^{+26}:\\ \;\;\;\;y \cdot -0.5\\ \mathbf{elif}\;y \leq 7.2 \cdot 10^{-85}:\\ \;\;\;\;x \cdot 1.5\\ \mathbf{else}:\\ \;\;\;\;y \cdot -0.5\\ \end{array} \]

Alternative 3: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \frac{x - y}{2} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ x (/ (- x y) 2.0)))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

   \[x + \frac{x - y}{2} \]

2. Final simplification 99.9%

   \[\leadsto x + \frac{x - y}{2} \]

Alternative 4: 50.6% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ y \cdot -0.5 \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_] := N[(y * -0.5), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[x + \frac{x - y}{2} \]

2. Taylor expanded in x around 0 50.8%

   \[\leadsto \color{blue}{-0.5 \cdot y} \]

3. Final simplification 50.8%

   \[\leadsto y \cdot -0.5 \]

Developer target: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 1.5 \cdot x - 0.5 \cdot y \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (- (* 1.5 x) (* 0.5 y)))

C

double code(double x, double y) {
	return (1.5 * x) - (0.5 * y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.5d0 * x) - (0.5d0 * y)
end function

Java

public static double code(double x, double y) {
	return (1.5 * x) - (0.5 * y);
}

Python

def code(x, y):
	return (1.5 * x) - (0.5 * y)

Julia

function code(x, y)
	return Float64(Float64(1.5 * x) - Float64(0.5 * y))
end

MATLAB

function tmp = code(x, y)
	tmp = (1.5 * x) - (0.5 * y);
end

Wolfram

code[x_, y_] := N[(N[(1.5 * x), $MachinePrecision] - N[(0.5 * y), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2023257 
(FPCore (x y)
  :name "Graphics.Rendering.Chart.Axis.Types:hBufferRect from Chart-1.5.3"
  :precision binary64

  :herbie-target
  (- (* 1.5 x) (* 0.5 y))

  (+ x (/ (- x y) 2.0)))