Falkner and Boettcher, Appendix A

Average Error: 2.0 → 0.1

Time: 8.6s

Precision: binary64

Math

\[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]

↓

\[\begin{array}{l} \mathbf{if}\;k \leq 9.708206840295397 \cdot 10^{+40}:\\ \;\;\;\;\frac{a}{-\mathsf{fma}\left(k, k + 10, 1\right)} \cdot \left(-{k}^{m}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{a}{k} \cdot \frac{{k}^{m}}{k}\\ \end{array} \]

FPCore

(FPCore (a k m)
 :precision binary64
 (/ (* a (pow k m)) (+ (+ 1.0 (* 10.0 k)) (* k k))))

↓

(FPCore (a k m)
 :precision binary64
 (if (<= k 9.708206840295397e+40)
   (* (/ a (- (fma k (+ k 10.0) 1.0))) (- (pow k m)))
   (* (/ a k) (/ (pow k m) k))))

C

double code(double a, double k, double m) {
	return (a * pow(k, m)) / ((1.0 + (10.0 * k)) + (k * k));
}

↓

double code(double a, double k, double m) {
	double tmp;
	if (k <= 9.708206840295397e+40) {
		tmp = (a / -fma(k, (k + 10.0), 1.0)) * -pow(k, m);
	} else {
		tmp = (a / k) * (pow(k, m) / k);
	}
	return tmp;
}

Julia

function code(a, k, m)
	return Float64(Float64(a * (k ^ m)) / Float64(Float64(1.0 + Float64(10.0 * k)) + Float64(k * k)))
end

↓

function code(a, k, m)
	tmp = 0.0
	if (k <= 9.708206840295397e+40)
		tmp = Float64(Float64(a / Float64(-fma(k, Float64(k + 10.0), 1.0))) * Float64(-(k ^ m)));
	else
		tmp = Float64(Float64(a / k) * Float64((k ^ m) / k));
	end
	return tmp
end

Wolfram

code[a_, k_, m_] := N[(N[(a * N[Power[k, m], $MachinePrecision]), $MachinePrecision] / N[(N[(1.0 + N[(10.0 * k), $MachinePrecision]), $MachinePrecision] + N[(k * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

↓

code[a_, k_, m_] := If[LessEqual[k, 9.708206840295397e+40], N[(N[(a / (-N[(k * N[(k + 10.0), $MachinePrecision] + 1.0), $MachinePrecision])), $MachinePrecision] * (-N[Power[k, m], $MachinePrecision])), $MachinePrecision], N[(N[(a / k), $MachinePrecision] * N[(N[Power[k, m], $MachinePrecision] / k), $MachinePrecision]), $MachinePrecision]]

Error

Bits error versus a

Bits error versus k

Bits error versus m

Derivation

1. Split input into 2 regimes

2. if k < 9.7082068402953972e40

   1. Initial program 0.1

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]

   2. Simplified 0.0

      \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\mathsf{fma}\left(k, k + 10, 1\right)}} \]

   3. Applied egg-rr 0.0

      \[\leadsto \color{blue}{a \cdot \frac{1}{\frac{\mathsf{fma}\left(k, k + 10, 1\right)}{{k}^{m}}}} \]

   4. Applied egg-rr 0.1

      \[\leadsto \color{blue}{\frac{\frac{a}{\sqrt{\frac{\mathsf{fma}\left(k, k + 10, 1\right)}{{k}^{m}}}}}{\sqrt{\frac{\mathsf{fma}\left(k, k + 10, 1\right)}{{k}^{m}}}}} \]

   5. Applied egg-rr 0.0

      \[\leadsto \color{blue}{\frac{a}{-\mathsf{fma}\left(k, k + 10, 1\right)} \cdot \left(-{k}^{m}\right)} \]

   if 9.7082068402953972e40 < k

   1. Initial program 5.9

      \[\frac{a \cdot {k}^{m}}{\left(1 + 10 \cdot k\right) + k \cdot k} \]

   2. Simplified 5.9

      \[\leadsto \color{blue}{\frac{a \cdot {k}^{m}}{\mathsf{fma}\left(k, k + 10, 1\right)}} \]

   3. Taylor expanded in k around inf 5.9

      \[\leadsto \color{blue}{\frac{a \cdot e^{-1 \cdot \left(\log \left(\frac{1}{k}\right) \cdot m\right)}}{{k}^{2}}} \]

   4. Simplified 0.1

      \[\leadsto \color{blue}{\frac{a}{k} \cdot \frac{{k}^{m}}{k}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 0.1

   \[\leadsto \begin{array}{l} \mathbf{if}\;k \leq 9.708206840295397 \cdot 10^{+40}:\\ \;\;\;\;\frac{a}{-\mathsf{fma}\left(k, k + 10, 1\right)} \cdot \left(-{k}^{m}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{a}{k} \cdot \frac{{k}^{m}}{k}\\ \end{array} \]

Reproduce

herbie shell --seed 2022153 
(FPCore (a k m)
  :name "Falkner and Boettcher, Appendix A"
  :precision binary64
  (/ (* a (pow k m)) (+ (+ 1.0 (* 10.0 k)) (* k k))))