xlohi (overflows)

Percentage Accurate: 3.1% → 19.4%

Time: 6.8s

Alternatives: 7

Speedup: 18.0×

Specification

\[lo < -1 \cdot 10^{+308} \land hi > 10^{+308}\]

Math

\[\begin{array}{l} \\ \frac{x - lo}{hi - lo} \end{array} \]

FPCore

(FPCore (lo hi x) :precision binary64 (/ (- x lo) (- hi lo)))

C

double code(double lo, double hi, double x) {
	return (x - lo) / (hi - lo);
}

Fortran

real(8) function code(lo, hi, x)
    real(8), intent (in) :: lo
    real(8), intent (in) :: hi
    real(8), intent (in) :: x
    code = (x - lo) / (hi - lo)
end function

Java

public static double code(double lo, double hi, double x) {
	return (x - lo) / (hi - lo);
}

Python

def code(lo, hi, x):
	return (x - lo) / (hi - lo)

Julia

function code(lo, hi, x)
	return Float64(Float64(x - lo) / Float64(hi - lo))
end

MATLAB

function tmp = code(lo, hi, x)
	tmp = (x - lo) / (hi - lo);
end

Wolfram

code[lo_, hi_, x_] := N[(N[(x - lo), $MachinePrecision] / N[(hi - lo), $MachinePrecision]), $MachinePrecision]

Initial Program: 3.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x - lo}{hi - lo} \end{array} \]

FPCore

(FPCore (lo hi x) :precision binary64 (/ (- x lo) (- hi lo)))

C

double code(double lo, double hi, double x) {
	return (x - lo) / (hi - lo);
}

Fortran

real(8) function code(lo, hi, x)
    real(8), intent (in) :: lo
    real(8), intent (in) :: hi
    real(8), intent (in) :: x
    code = (x - lo) / (hi - lo)
end function

Java

public static double code(double lo, double hi, double x) {
	return (x - lo) / (hi - lo);
}

Python

def code(lo, hi, x):
	return (x - lo) / (hi - lo)

Julia

function code(lo, hi, x)
	return Float64(Float64(x - lo) / Float64(hi - lo))
end

MATLAB

function tmp = code(lo, hi, x)
	tmp = (x - lo) / (hi - lo);
end

Wolfram

code[lo_, hi_, x_] := N[(N[(x - lo), $MachinePrecision] / N[(hi - lo), $MachinePrecision]), $MachinePrecision]

Alternative 1: 19.4% accurate, 0.1× speedup

Math

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

FPCore

(FPCore (lo hi x)
 :precision binary64
 (fma (/ (* (- (/ (/ x lo) lo) (pow lo -1.0)) (- hi)) lo) hi (/ (- x) lo)))

C

double code(double lo, double hi, double x) {
	return fma((((((x / lo) / lo) - pow(lo, -1.0)) * -hi) / lo), hi, (-x / lo));
}

Julia

function code(lo, hi, x)
	return fma(Float64(Float64(Float64(Float64(Float64(x / lo) / lo) - (lo ^ -1.0)) * Float64(-hi)) / lo), hi, Float64(Float64(-x) / lo))
end

Wolfram

code[lo_, hi_, x_] := N[(N[(N[(N[(N[(N[(x / lo), $MachinePrecision] / lo), $MachinePrecision] - N[Power[lo, -1.0], $MachinePrecision]), $MachinePrecision] * (-hi)), $MachinePrecision] / lo), $MachinePrecision] * hi + N[((-x) / lo), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 3.1%

   \[\frac{x - lo}{hi - lo} \]
2. Add Preprocessing

3. Taylor expanded in hi around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

5. Applied rewrites 18.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{lo} - \frac{\frac{x}{lo}}{lo}, hi, 1\right)}{lo} - \frac{\frac{x}{lo}}{lo}, hi, \frac{\mathsf{fma}\left(-1, x, lo\right)}{lo}\right)} \]

6. Taylor expanded in lo around 0

   \[\leadsto \mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{lo} - \frac{\frac{x}{lo}}{lo}, hi, 1\right)}{lo} - \frac{\frac{x}{lo}}{lo}, hi, \frac{-1 \cdot x}{lo}\right) \]
7. Step-by-step derivation

8. Applied rewrites 10.9%

   \[\leadsto \mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{lo} - \frac{\frac{x}{lo}}{lo}, hi, 1\right)}{lo} - \frac{\frac{x}{lo}}{lo}, hi, \frac{-x}{lo}\right) \]

10. Applied rewrites 10.9%

    \[\leadsto \mathsf{fma}\left(\frac{\left(-\left(-hi\right) \cdot \frac{\frac{lo - x}{lo}}{lo}\right) + \frac{lo - x}{lo}}{-\left(-lo\right)}, hi, \frac{-x}{lo}\right) \]

11. Taylor expanded in hi around -inf

    \[\leadsto \mathsf{fma}\left(\frac{-1 \cdot \left(hi \cdot \left(\frac{x}{{lo}^{2}} - \frac{1}{lo}\right)\right)}{-\left(-lo\right)}, hi, \frac{-x}{lo}\right) \]
12. Step-by-step derivation

13. Applied rewrites 19.3%

    \[\leadsto \mathsf{fma}\left(\frac{\left(\frac{\frac{x}{lo}}{lo} - \frac{1}{lo}\right) \cdot \left(-hi\right)}{-\left(-lo\right)}, hi, \frac{-x}{lo}\right) \]

14. Final simplification 19.3%

    \[\leadsto \mathsf{fma}\left(\frac{\left(\frac{\frac{x}{lo}}{lo} - {lo}^{-1}\right) \cdot \left(-hi\right)}{lo}, hi, \frac{-x}{lo}\right) \]
15. Add Preprocessing

Alternative 2: 19.4% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\frac{\frac{\frac{lo - x}{lo}}{lo} \cdot hi - \frac{x}{lo}}{lo}, hi, \frac{-x}{lo}\right) \end{array} \]

FPCore

(FPCore (lo hi x)
 :precision binary64
 (fma (/ (- (* (/ (/ (- lo x) lo) lo) hi) (/ x lo)) lo) hi (/ (- x) lo)))

C

double code(double lo, double hi, double x) {
	return fma(((((((lo - x) / lo) / lo) * hi) - (x / lo)) / lo), hi, (-x / lo));
}

Julia

function code(lo, hi, x)
	return fma(Float64(Float64(Float64(Float64(Float64(Float64(lo - x) / lo) / lo) * hi) - Float64(x / lo)) / lo), hi, Float64(Float64(-x) / lo))
end

Wolfram

code[lo_, hi_, x_] := N[(N[(N[(N[(N[(N[(N[(lo - x), $MachinePrecision] / lo), $MachinePrecision] / lo), $MachinePrecision] * hi), $MachinePrecision] - N[(x / lo), $MachinePrecision]), $MachinePrecision] / lo), $MachinePrecision] * hi + N[((-x) / lo), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 3.1%

   \[\frac{x - lo}{hi - lo} \]
2. Add Preprocessing

3. Taylor expanded in hi around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

5. Applied rewrites 18.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{lo} - \frac{\frac{x}{lo}}{lo}, hi, 1\right)}{lo} - \frac{\frac{x}{lo}}{lo}, hi, \frac{\mathsf{fma}\left(-1, x, lo\right)}{lo}\right)} \]

6. Taylor expanded in lo around 0

   \[\leadsto \mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{lo} - \frac{\frac{x}{lo}}{lo}, hi, 1\right)}{lo} - \frac{\frac{x}{lo}}{lo}, hi, \frac{-1 \cdot x}{lo}\right) \]
7. Step-by-step derivation

8. Applied rewrites 10.9%

   \[\leadsto \mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{lo} - \frac{\frac{x}{lo}}{lo}, hi, 1\right)}{lo} - \frac{\frac{x}{lo}}{lo}, hi, \frac{-x}{lo}\right) \]

9. Taylor expanded in hi around inf

   \[\leadsto \mathsf{fma}\left(\frac{hi \cdot \left(\frac{1}{lo} - \frac{x}{{lo}^{2}}\right)}{lo} - \frac{\frac{x}{lo}}{lo}, hi, \frac{-x}{lo}\right) \]
10. Step-by-step derivation

11. Applied rewrites 19.3%

    \[\leadsto \mathsf{fma}\left(\frac{\frac{\frac{lo - x}{lo}}{lo} \cdot hi}{lo} - \frac{\frac{x}{lo}}{lo}, hi, \frac{-x}{lo}\right) \]
12. Step-by-step derivation

13. Applied rewrites 19.3%

    \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\frac{\frac{lo - x}{lo}}{lo} \cdot hi - \frac{x}{lo}}{lo}, hi, \frac{-x}{lo}\right)} \]
14. Add Preprocessing

Alternative 3: 19.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \left(\frac{\frac{lo - x}{lo}}{lo} \cdot hi\right) \cdot \frac{hi}{lo} \end{array} \]

FPCore

(FPCore (lo hi x)
 :precision binary64
 (* (* (/ (/ (- lo x) lo) lo) hi) (/ hi lo)))

C

double code(double lo, double hi, double x) {
	return ((((lo - x) / lo) / lo) * hi) * (hi / lo);
}

Fortran

real(8) function code(lo, hi, x)
    real(8), intent (in) :: lo
    real(8), intent (in) :: hi
    real(8), intent (in) :: x
    code = ((((lo - x) / lo) / lo) * hi) * (hi / lo)
end function

Java

public static double code(double lo, double hi, double x) {
	return ((((lo - x) / lo) / lo) * hi) * (hi / lo);
}

Python

def code(lo, hi, x):
	return ((((lo - x) / lo) / lo) * hi) * (hi / lo)

Julia

function code(lo, hi, x)
	return Float64(Float64(Float64(Float64(Float64(lo - x) / lo) / lo) * hi) * Float64(hi / lo))
end

MATLAB

function tmp = code(lo, hi, x)
	tmp = ((((lo - x) / lo) / lo) * hi) * (hi / lo);
end

Wolfram

code[lo_, hi_, x_] := N[(N[(N[(N[(N[(lo - x), $MachinePrecision] / lo), $MachinePrecision] / lo), $MachinePrecision] * hi), $MachinePrecision] * N[(hi / lo), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 3.1%

   \[\frac{x - lo}{hi - lo} \]
2. Add Preprocessing

3. Taylor expanded in hi around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

5. Applied rewrites 18.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{lo} - \frac{\frac{x}{lo}}{lo}, hi, 1\right)}{lo} - \frac{\frac{x}{lo}}{lo}, hi, \frac{\mathsf{fma}\left(-1, x, lo\right)}{lo}\right)} \]

6. Taylor expanded in hi around inf

   \[\leadsto {hi}^{2} \cdot \color{blue}{\left(\frac{1}{{lo}^{2}} - \frac{x}{{lo}^{3}}\right)} \]
7. Step-by-step derivation

8. Applied rewrites 19.3%

   \[\leadsto \frac{\frac{\frac{lo - x}{lo}}{lo} \cdot hi}{lo} \cdot \color{blue}{hi} \]
9. Step-by-step derivation

10. Applied rewrites 19.3%

    \[\leadsto \left(\frac{\frac{lo - x}{lo}}{lo} \cdot hi\right) \cdot \frac{hi}{\color{blue}{lo}} \]
11. Add Preprocessing

Alternative 4: 19.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{hi}{lo}}{lo} \cdot hi \end{array} \]

FPCore

(FPCore (lo hi x) :precision binary64 (* (/ (/ hi lo) lo) hi))

C

double code(double lo, double hi, double x) {
	return ((hi / lo) / lo) * hi;
}

Fortran

real(8) function code(lo, hi, x)
    real(8), intent (in) :: lo
    real(8), intent (in) :: hi
    real(8), intent (in) :: x
    code = ((hi / lo) / lo) * hi
end function

Java

public static double code(double lo, double hi, double x) {
	return ((hi / lo) / lo) * hi;
}

Python

def code(lo, hi, x):
	return ((hi / lo) / lo) * hi

Julia

function code(lo, hi, x)
	return Float64(Float64(Float64(hi / lo) / lo) * hi)
end

MATLAB

function tmp = code(lo, hi, x)
	tmp = ((hi / lo) / lo) * hi;
end

Wolfram

code[lo_, hi_, x_] := N[(N[(N[(hi / lo), $MachinePrecision] / lo), $MachinePrecision] * hi), $MachinePrecision]

Derivation

1. Initial program 3.1%

   \[\frac{x - lo}{hi - lo} \]
2. Add Preprocessing

3. Taylor expanded in hi around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

5. Applied rewrites 18.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{1}{lo} - \frac{\frac{x}{lo}}{lo}, hi, 1\right)}{lo} - \frac{\frac{x}{lo}}{lo}, hi, \frac{\mathsf{fma}\left(-1, x, lo\right)}{lo}\right)} \]

6. Taylor expanded in hi around inf

   \[\leadsto {hi}^{2} \cdot \color{blue}{\left(\frac{1}{{lo}^{2}} - \frac{x}{{lo}^{3}}\right)} \]
7. Step-by-step derivation

8. Applied rewrites 19.3%

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

9. Taylor expanded in lo around inf

   \[\leadsto \frac{\frac{hi}{lo}}{lo} \cdot hi \]
10. Step-by-step derivation

11. Applied rewrites 19.3%

    \[\leadsto \frac{\frac{hi}{lo}}{lo} \cdot hi \]
12. Add Preprocessing

Alternative 5: 18.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \frac{x - lo}{hi} \end{array} \]

FPCore

(FPCore (lo hi x) :precision binary64 (/ (- x lo) hi))

C

double code(double lo, double hi, double x) {
	return (x - lo) / hi;
}

Fortran

real(8) function code(lo, hi, x)
    real(8), intent (in) :: lo
    real(8), intent (in) :: hi
    real(8), intent (in) :: x
    code = (x - lo) / hi
end function

Java

public static double code(double lo, double hi, double x) {
	return (x - lo) / hi;
}

Python

def code(lo, hi, x):
	return (x - lo) / hi

Julia

function code(lo, hi, x)
	return Float64(Float64(x - lo) / hi)
end

MATLAB

function tmp = code(lo, hi, x)
	tmp = (x - lo) / hi;
end

Wolfram

code[lo_, hi_, x_] := N[(N[(x - lo), $MachinePrecision] / hi), $MachinePrecision]

Derivation

1. Initial program 3.1%

   \[\frac{x - lo}{hi - lo} \]
2. Add Preprocessing

3. Taylor expanded in hi around inf

   \[\leadsto \color{blue}{\frac{x - lo}{hi}} \]
4. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{x - lo}{hi}} \]

   2. lower--.f64 18.8

      \[\leadsto \frac{\color{blue}{x - lo}}{hi} \]

5. Applied rewrites 18.8%

   \[\leadsto \color{blue}{\frac{x - lo}{hi}} \]
6. Add Preprocessing

Alternative 6: 18.8% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \frac{-lo}{hi} \end{array} \]

FPCore

(FPCore (lo hi x) :precision binary64 (/ (- lo) hi))

C

double code(double lo, double hi, double x) {
	return -lo / hi;
}

Fortran

real(8) function code(lo, hi, x)
    real(8), intent (in) :: lo
    real(8), intent (in) :: hi
    real(8), intent (in) :: x
    code = -lo / hi
end function

Java

public static double code(double lo, double hi, double x) {
	return -lo / hi;
}

Python

def code(lo, hi, x):
	return -lo / hi

Julia

function code(lo, hi, x)
	return Float64(Float64(-lo) / hi)
end

MATLAB

function tmp = code(lo, hi, x)
	tmp = -lo / hi;
end

Wolfram

code[lo_, hi_, x_] := N[((-lo) / hi), $MachinePrecision]

Derivation

1. Initial program 3.1%

   \[\frac{x - lo}{hi - lo} \]
2. Add Preprocessing

3. Taylor expanded in lo around 0

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

   1. mul-1-neg N/A

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

   2. distribute-rgt-in N/A

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

   3. distribute-neg-in N/A

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

   4. *-commutative N/A

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

   5. associate-*l/ N/A

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

   6. *-lft-identity N/A

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

   7. mul-1-neg N/A

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

   8. associate-+l+ N/A

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

5. Applied rewrites 18.8%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{lo}{hi}, \frac{x}{hi}, \frac{x - lo}{hi}\right)} \]

6. Taylor expanded in lo around -inf

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

8. Applied rewrites 18.8%

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

9. Taylor expanded in hi around inf

   \[\leadsto -1 \cdot \frac{lo}{\color{blue}{hi}} \]
10. Step-by-step derivation

11. Applied rewrites 18.8%

    \[\leadsto \frac{lo}{-hi} \]

12. Final simplification 18.8%

    \[\leadsto \frac{-lo}{hi} \]
13. Add Preprocessing

Alternative 7: 18.7% accurate, 18.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (lo hi x) :precision binary64 1.0)

C

double code(double lo, double hi, double x) {
	return 1.0;
}

Fortran

real(8) function code(lo, hi, x)
    real(8), intent (in) :: lo
    real(8), intent (in) :: hi
    real(8), intent (in) :: x
    code = 1.0d0
end function

Java

public static double code(double lo, double hi, double x) {
	return 1.0;
}

Python

def code(lo, hi, x):
	return 1.0

Julia

function code(lo, hi, x)
	return 1.0
end

MATLAB

function tmp = code(lo, hi, x)
	tmp = 1.0;
end

Wolfram

code[lo_, hi_, x_] := 1.0

Derivation

1. Initial program 3.1%

   \[\frac{x - lo}{hi - lo} \]
2. Add Preprocessing

3. Taylor expanded in lo around inf

   \[\leadsto \color{blue}{1} \]
4. Step-by-step derivation

5. Applied rewrites 18.7%

   \[\leadsto \color{blue}{1} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024337 
(FPCore (lo hi x)
  :name "xlohi (overflows)"
  :precision binary64
  :pre (and (< lo -1e+308) (> hi 1e+308))
  (/ (- x lo) (- hi lo)))