From Rump in a 1983 paper, rewritten

Percentage Accurate: 3.1% → 100.0%

Time: 3.9s

Alternatives: 2

Speedup: 11.6×

Specification

\[x = 10864 \land y = 18817\]

Math

\[\begin{array}{l} \\ 9 \cdot {x}^{4} - \left(y \cdot y\right) \cdot \left(y \cdot y - 2\right) \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (- (* 9.0 (pow x 4.0)) (* (* y y) (- (* y y) 2.0))))

C

double code(double x, double y) {
	return (9.0 * pow(x, 4.0)) - ((y * y) * ((y * y) - 2.0));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (9.0d0 * (x ** 4.0d0)) - ((y * y) * ((y * y) - 2.0d0))
end function

Java

public static double code(double x, double y) {
	return (9.0 * Math.pow(x, 4.0)) - ((y * y) * ((y * y) - 2.0));
}

Python

def code(x, y):
	return (9.0 * math.pow(x, 4.0)) - ((y * y) * ((y * y) - 2.0))

Julia

function code(x, y)
	return Float64(Float64(9.0 * (x ^ 4.0)) - Float64(Float64(y * y) * Float64(Float64(y * y) - 2.0)))
end

MATLAB

function tmp = code(x, y)
	tmp = (9.0 * (x ^ 4.0)) - ((y * y) * ((y * y) - 2.0));
end

Wolfram

code[x_, y_] := N[(N[(9.0 * N[Power[x, 4.0], $MachinePrecision]), $MachinePrecision] - N[(N[(y * y), $MachinePrecision] * N[(N[(y * y), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 3.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 9 \cdot {x}^{4} - \left(y \cdot y\right) \cdot \left(y \cdot y - 2\right) \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (- (* 9.0 (pow x 4.0)) (* (* y y) (- (* y y) 2.0))))

C

double code(double x, double y) {
	return (9.0 * pow(x, 4.0)) - ((y * y) * ((y * y) - 2.0));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (9.0d0 * (x ** 4.0d0)) - ((y * y) * ((y * y) - 2.0d0))
end function

Java

public static double code(double x, double y) {
	return (9.0 * Math.pow(x, 4.0)) - ((y * y) * ((y * y) - 2.0));
}

Python

def code(x, y):
	return (9.0 * math.pow(x, 4.0)) - ((y * y) * ((y * y) - 2.0))

Julia

function code(x, y)
	return Float64(Float64(9.0 * (x ^ 4.0)) - Float64(Float64(y * y) * Float64(Float64(y * y) - 2.0)))
end

MATLAB

function tmp = code(x, y)
	tmp = (9.0 * (x ^ 4.0)) - ((y * y) * ((y * y) - 2.0));
end

Wolfram

code[x_, y_] := N[(N[(9.0 * N[Power[x, 4.0], $MachinePrecision]), $MachinePrecision] - N[(N[(y * y), $MachinePrecision] * N[(N[(y * y), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 3.2× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\mathsf{fma}\left(-y, y, 2\right), y \cdot y, \left(\left(9 \cdot \left(x \cdot x\right)\right) \cdot x\right) \cdot x\right) \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (fma (fma (- y) y 2.0) (* y y) (* (* (* 9.0 (* x x)) x) x)))

C

double code(double x, double y) {
	return fma(fma(-y, y, 2.0), (y * y), (((9.0 * (x * x)) * x) * x));
}

Julia

function code(x, y)
	return fma(fma(Float64(-y), y, 2.0), Float64(y * y), Float64(Float64(Float64(9.0 * Float64(x * x)) * x) * x))
end

Wolfram

code[x_, y_] := N[(N[((-y) * y + 2.0), $MachinePrecision] * N[(y * y), $MachinePrecision] + N[(N[(N[(9.0 * N[(x * x), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 3.1%

   \[9 \cdot {x}^{4} - \left(y \cdot y\right) \cdot \left(y \cdot y - 2\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift--.f64 N/A

      \[\leadsto \color{blue}{9 \cdot {x}^{4} - \left(y \cdot y\right) \cdot \left(y \cdot y - 2\right)} \]

   2. sub-neg N/A

      \[\leadsto \color{blue}{9 \cdot {x}^{4} + \left(\mathsf{neg}\left(\left(y \cdot y\right) \cdot \left(y \cdot y - 2\right)\right)\right)} \]

   3. +-commutative N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\left(y \cdot y\right) \cdot \left(y \cdot y - 2\right)\right)\right) + 9 \cdot {x}^{4}} \]

   4. lift-*.f64 N/A

      \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\left(y \cdot y\right) \cdot \left(y \cdot y - 2\right)}\right)\right) + 9 \cdot {x}^{4} \]

   5. *-commutative N/A

      \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\left(y \cdot y - 2\right) \cdot \left(y \cdot y\right)}\right)\right) + 9 \cdot {x}^{4} \]

   6. distribute-lft-neg-in N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\left(y \cdot y - 2\right)\right)\right) \cdot \left(y \cdot y\right)} + 9 \cdot {x}^{4} \]

   7. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{neg}\left(\left(y \cdot y - 2\right)\right), y \cdot y, 9 \cdot {x}^{4}\right)} \]

   8. lift--.f64 N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{neg}\left(\color{blue}{\left(y \cdot y - 2\right)}\right), y \cdot y, 9 \cdot {x}^{4}\right) \]

   9. sub-neg N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{neg}\left(\color{blue}{\left(y \cdot y + \left(\mathsf{neg}\left(2\right)\right)\right)}\right), y \cdot y, 9 \cdot {x}^{4}\right) \]

   10. distribute-neg-in N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\mathsf{neg}\left(y \cdot y\right)\right) + \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(2\right)\right)\right)\right)}, y \cdot y, 9 \cdot {x}^{4}\right) \]

   11. lift-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\left(\mathsf{neg}\left(\color{blue}{y \cdot y}\right)\right) + \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(2\right)\right)\right)\right), y \cdot y, 9 \cdot {x}^{4}\right) \]

   12. distribute-lft-neg-in N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\mathsf{neg}\left(y\right)\right) \cdot y} + \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(2\right)\right)\right)\right), y \cdot y, 9 \cdot {x}^{4}\right) \]

   13. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(\left(\mathsf{neg}\left(y\right)\right) \cdot y + \left(\mathsf{neg}\left(\color{blue}{-2}\right)\right), y \cdot y, 9 \cdot {x}^{4}\right) \]

   14. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(\left(\mathsf{neg}\left(y\right)\right) \cdot y + \color{blue}{2}, y \cdot y, 9 \cdot {x}^{4}\right) \]

   15. lower-fma.f64 N/A

       \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\mathsf{neg}\left(y\right), y, 2\right)}, y \cdot y, 9 \cdot {x}^{4}\right) \]

   16. lower-neg.f64 100.0

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{-y}, y, 2\right), y \cdot y, 9 \cdot {x}^{4}\right) \]

   17. lift-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{neg}\left(y\right), y, 2\right), y \cdot y, \color{blue}{9 \cdot {x}^{4}}\right) \]

   18. lift-pow.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{neg}\left(y\right), y, 2\right), y \cdot y, 9 \cdot \color{blue}{{x}^{4}}\right) \]

   19. sqr-pow N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{neg}\left(y\right), y, 2\right), y \cdot y, 9 \cdot \color{blue}{\left({x}^{\left(\frac{4}{2}\right)} \cdot {x}^{\left(\frac{4}{2}\right)}\right)}\right) \]

   20. associate-*r* N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{neg}\left(y\right), y, 2\right), y \cdot y, \color{blue}{\left(9 \cdot {x}^{\left(\frac{4}{2}\right)}\right) \cdot {x}^{\left(\frac{4}{2}\right)}}\right) \]

   21. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{neg}\left(y\right), y, 2\right), y \cdot y, \left(9 \cdot {x}^{\left(\frac{4}{2}\right)}\right) \cdot {x}^{\color{blue}{2}}\right) \]

   22. unpow2 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{neg}\left(y\right), y, 2\right), y \cdot y, \left(9 \cdot {x}^{\left(\frac{4}{2}\right)}\right) \cdot \color{blue}{\left(x \cdot x\right)}\right) \]

4. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(-y, y, 2\right), y \cdot y, \left(\left(\left(x \cdot x\right) \cdot 9\right) \cdot x\right) \cdot x\right)} \]

5. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(-y, y, 2\right), y \cdot y, \left(\left(9 \cdot \left(x \cdot x\right)\right) \cdot x\right) \cdot x\right) \]
6. Add Preprocessing

Alternative 2: 11.1% accurate, 11.6× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (* (* 2.0 y) y))

C

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

Fortran

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

Java

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

Python

def code(x, y):
	return (2.0 * y) * y

Julia

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

MATLAB

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

Wolfram

code[x_, y_] := N[(N[(2.0 * y), $MachinePrecision] * y), $MachinePrecision]

Derivation

1. Initial program 3.1%

   \[9 \cdot {x}^{4} - \left(y \cdot y\right) \cdot \left(y \cdot y - 2\right) \]
2. Add Preprocessing

3. Taylor expanded in y around inf

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

   1. metadata-eval N/A

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

   2. pow-sqr N/A

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

   3. associate-*l* N/A

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

   4. sub-neg N/A

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

   5. metadata-eval N/A

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

   6. +-commutative N/A

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

   7. distribute-rgt-in N/A

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

   8. associate-*l* N/A

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

   9. lft-mult-inverse N/A

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

   10. metadata-eval N/A

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

   11. unpow2 N/A

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

   12. +-commutative N/A

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

   13. associate-*l* N/A

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

   14. *-commutative N/A

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

   15. lower-*.f64 N/A

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

5. Applied rewrites 1.5%

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

6. Taylor expanded in y around 0

   \[\leadsto \left(2 \cdot y\right) \cdot y \]
7. Step-by-step derivation

8. Applied rewrites 11.1%

   \[\leadsto \left(2 \cdot y\right) \cdot y \]
9. Add Preprocessing

Reproduce

herbie shell --seed 2024240 
(FPCore (x y)
  :name "From Rump in a 1983 paper, rewritten"
  :precision binary64
  :pre (and (== x 10864.0) (== y 18817.0))
  (- (* 9.0 (pow x 4.0)) (* (* y y) (- (* y y) 2.0))))