ENA, Section 1.4, Mentioned, B

Percentage Accurate: 87.7% → 99.6%

Time: 14.4s

Alternatives: 4

Speedup: 1.1×

Specification

\[0.999 \leq x \land x \leq 1.001\]

Math

\[\begin{array}{l} \\ \frac{10}{1 - x \cdot x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 10.0 (- 1.0 (* x x))))

C

double code(double x) {
	return 10.0 / (1.0 - (x * x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 10.0d0 / (1.0d0 - (x * x))
end function

Java

public static double code(double x) {
	return 10.0 / (1.0 - (x * x));
}

Python

def code(x):
	return 10.0 / (1.0 - (x * x))

Julia

function code(x)
	return Float64(10.0 / Float64(1.0 - Float64(x * x)))
end

MATLAB

function tmp = code(x)
	tmp = 10.0 / (1.0 - (x * x));
end

Wolfram

code[x_] := N[(10.0 / N[(1.0 - N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 87.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{10}{1 - x \cdot x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 10.0 (- 1.0 (* x x))))

C

double code(double x) {
	return 10.0 / (1.0 - (x * x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 10.0d0 / (1.0d0 - (x * x))
end function

Java

public static double code(double x) {
	return 10.0 / (1.0 - (x * x));
}

Python

def code(x):
	return 10.0 / (1.0 - (x * x))

Julia

function code(x)
	return Float64(10.0 / Float64(1.0 - Float64(x * x)))
end

MATLAB

function tmp = code(x)
	tmp = 10.0 / (1.0 - (x * x));
end

Wolfram

code[x_] := N[(10.0 / N[(1.0 - N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \frac{-10}{\mathsf{fma}\left(x, x, -1\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ -10.0 (fma x x -1.0)))

C

double code(double x) {
	return -10.0 / fma(x, x, -1.0);
}

Julia

function code(x)
	return Float64(-10.0 / fma(x, x, -1.0))
end

Wolfram

code[x_] := N[(-10.0 / N[(x * x + -1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 87.5%

   \[\frac{10}{1 - x \cdot x} \]
2. Add Preprocessing

4. Applied egg-rr 99.5%

   \[\leadsto \color{blue}{\frac{-10}{\mathsf{fma}\left(x, x, -1\right)}} \]
5. Add Preprocessing

Alternative 2: 13.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(x, x, 1\right) \cdot 10\\ \mathbf{else}:\\ \;\;\;\;\frac{-10}{x \cdot x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (* x x) 1.0) (* (fma x x 1.0) 10.0) (/ -10.0 (* x x))))

C

double code(double x) {
	double tmp;
	if ((x * x) <= 1.0) {
		tmp = fma(x, x, 1.0) * 10.0;
	} else {
		tmp = -10.0 / (x * x);
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (Float64(x * x) <= 1.0)
		tmp = Float64(fma(x, x, 1.0) * 10.0);
	else
		tmp = Float64(-10.0 / Float64(x * x));
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[N[(x * x), $MachinePrecision], 1.0], N[(N[(x * x + 1.0), $MachinePrecision] * 10.0), $MachinePrecision], N[(-10.0 / N[(x * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x x) < 1

   1. Initial program 87.8%

      \[\frac{10}{1 - x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{10 + 10 \cdot {x}^{2}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{10 \cdot {x}^{2} + 10} \]

      2. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(10, {x}^{2}, 10\right)} \]

      3. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(10, \color{blue}{x \cdot x}, 10\right) \]

      4. lower-*.f64 13.7

         \[\leadsto \mathsf{fma}\left(10, \color{blue}{x \cdot x}, 10\right) \]

   5. Simplified 13.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(10, x \cdot x, 10\right)} \]
   6. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto 10 \cdot \color{blue}{\left(x \cdot x\right)} + 10 \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot 10} + 10 \]

      3. distribute-lft1-in N/A

         \[\leadsto \color{blue}{\left(x \cdot x + 1\right) \cdot 10} \]

      4. metadata-eval N/A

         \[\leadsto \left(x \cdot x + \color{blue}{\left(\mathsf{neg}\left(-1\right)\right)}\right) \cdot 10 \]

      5. sub-neg N/A

         \[\leadsto \color{blue}{\left(x \cdot x - -1\right)} \cdot 10 \]

      6. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(x \cdot x - -1\right) \cdot 10} \]

      7. sub-neg N/A

         \[\leadsto \color{blue}{\left(x \cdot x + \left(\mathsf{neg}\left(-1\right)\right)\right)} \cdot 10 \]

      8. lift-*.f64 N/A

         \[\leadsto \left(\color{blue}{x \cdot x} + \left(\mathsf{neg}\left(-1\right)\right)\right) \cdot 10 \]

      9. metadata-eval N/A

         \[\leadsto \left(x \cdot x + \color{blue}{1}\right) \cdot 10 \]

      10. lower-fma.f64 13.7

          \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, 1\right)} \cdot 10 \]

   7. Applied egg-rr 13.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, 1\right) \cdot 10} \]

   if 1 < (*.f64 x x)

   1. Initial program 87.0%

      \[\frac{10}{1 - x \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{-10}{{x}^{2}}} \]
   4. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{-10}{{x}^{2}}} \]

      2. unpow2 N/A

         \[\leadsto \frac{-10}{\color{blue}{x \cdot x}} \]

      3. lower-*.f64 13.5

         \[\leadsto \frac{-10}{\color{blue}{x \cdot x}} \]

   5. Simplified 13.5%

      \[\leadsto \color{blue}{\frac{-10}{x \cdot x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 9.5% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, x, 1\right) \cdot 10 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (fma x x 1.0) 10.0))

C

double code(double x) {
	return fma(x, x, 1.0) * 10.0;
}

Julia

function code(x)
	return Float64(fma(x, x, 1.0) * 10.0)
end

Wolfram

code[x_] := N[(N[(x * x + 1.0), $MachinePrecision] * 10.0), $MachinePrecision]

Derivation

1. Initial program 87.5%

   \[\frac{10}{1 - x \cdot x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{10 + 10 \cdot {x}^{2}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \color{blue}{10 \cdot {x}^{2} + 10} \]

   2. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(10, {x}^{2}, 10\right)} \]

   3. unpow2 N/A

      \[\leadsto \mathsf{fma}\left(10, \color{blue}{x \cdot x}, 10\right) \]

   4. lower-*.f64 10.1

      \[\leadsto \mathsf{fma}\left(10, \color{blue}{x \cdot x}, 10\right) \]

5. Simplified 10.1%

   \[\leadsto \color{blue}{\mathsf{fma}\left(10, x \cdot x, 10\right)} \]
6. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto 10 \cdot \color{blue}{\left(x \cdot x\right)} + 10 \]

   2. *-commutative N/A

      \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot 10} + 10 \]

   3. distribute-lft1-in N/A

      \[\leadsto \color{blue}{\left(x \cdot x + 1\right) \cdot 10} \]

   4. metadata-eval N/A

      \[\leadsto \left(x \cdot x + \color{blue}{\left(\mathsf{neg}\left(-1\right)\right)}\right) \cdot 10 \]

   5. sub-neg N/A

      \[\leadsto \color{blue}{\left(x \cdot x - -1\right)} \cdot 10 \]

   6. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(x \cdot x - -1\right) \cdot 10} \]

   7. sub-neg N/A

      \[\leadsto \color{blue}{\left(x \cdot x + \left(\mathsf{neg}\left(-1\right)\right)\right)} \cdot 10 \]

   8. lift-*.f64 N/A

      \[\leadsto \left(\color{blue}{x \cdot x} + \left(\mathsf{neg}\left(-1\right)\right)\right) \cdot 10 \]

   9. metadata-eval N/A

      \[\leadsto \left(x \cdot x + \color{blue}{1}\right) \cdot 10 \]

   10. lower-fma.f64 10.1

       \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, 1\right)} \cdot 10 \]

7. Applied egg-rr 10.1%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, 1\right) \cdot 10} \]
8. Add Preprocessing

Alternative 4: 9.4% accurate, 20.0× speedup

Math

\[\begin{array}{l} \\ 10 \end{array} \]

FPCore

(FPCore (x) :precision binary64 10.0)

C

double code(double x) {
	return 10.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 10.0d0
end function

Java

public static double code(double x) {
	return 10.0;
}

Python

def code(x):
	return 10.0

Julia

function code(x)
	return 10.0
end

MATLAB

function tmp = code(x)
	tmp = 10.0;
end

Wolfram

code[x_] := 10.0

Derivation

1. Initial program 87.5%

   \[\frac{10}{1 - x \cdot x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{10} \]
4. Step-by-step derivation

5. Simplified 9.9%

   \[\leadsto \color{blue}{10} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024207 
(FPCore (x)
  :name "ENA, Section 1.4, Mentioned, B"
  :precision binary64
  :pre (and (<= 0.999 x) (<= x 1.001))
  (/ 10.0 (- 1.0 (* x x))))