Disney BSSRDF, sample scattering profile, lower

Percentage Accurate: 61.6% → 99.4%

Time: 9.1s

Alternatives: 7

Speedup: 21.8×

Specification

\[\left(0 \leq s \land s \leq 256\right) \land \left(2.328306437 \cdot 10^{-10} \leq u \land u \leq 0.25\right)\]

Math

\[\begin{array}{l} \\ s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right) \end{array} \]

FPCore

(FPCore (s u) :precision binary32 (* s (log (/ 1.0 (- 1.0 (* 4.0 u))))))

C

float code(float s, float u) {
	return s * logf((1.0f / (1.0f - (4.0f * u))));
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = s * log((1.0e0 / (1.0e0 - (4.0e0 * u))))
end function

Julia

function code(s, u)
	return Float32(s * log(Float32(Float32(1.0) / Float32(Float32(1.0) - Float32(Float32(4.0) * u)))))
end

MATLAB

function tmp = code(s, u)
	tmp = s * log((single(1.0) / (single(1.0) - (single(4.0) * u))));
end

Initial Program: 61.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right) \end{array} \]

FPCore

(FPCore (s u) :precision binary32 (* s (log (/ 1.0 (- 1.0 (* 4.0 u))))))

C

float code(float s, float u) {
	return s * logf((1.0f / (1.0f - (4.0f * u))));
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = s * log((1.0e0 / (1.0e0 - (4.0e0 * u))))
end function

Julia

function code(s, u)
	return Float32(s * log(Float32(Float32(1.0) / Float32(Float32(1.0) - Float32(Float32(4.0) * u)))))
end

MATLAB

function tmp = code(s, u)
	tmp = s * log((single(1.0) / (single(1.0) - (single(4.0) * u))));
end

Alternative 1: 99.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{log1p}\left(u \cdot -4\right) \cdot \left(-s\right) \end{array} \]

FPCore

(FPCore (s u) :precision binary32 (* (log1p (* u -4.0)) (- s)))

C

float code(float s, float u) {
	return log1pf((u * -4.0f)) * -s;
}

Julia

function code(s, u)
	return Float32(log1p(Float32(u * Float32(-4.0))) * Float32(-s))
end

Derivation

1. Initial program 59.7%

   \[s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right) \]
2. Step-by-step derivation

   1. *-commutative 59.7%

      \[\leadsto \color{blue}{\log \left(\frac{1}{1 - 4 \cdot u}\right) \cdot s} \]

   2. log-rec 62.9%

      \[\leadsto \color{blue}{\left(-\log \left(1 - 4 \cdot u\right)\right)} \cdot s \]

   3. distribute-lft-neg-out 62.9%

      \[\leadsto \color{blue}{-\log \left(1 - 4 \cdot u\right) \cdot s} \]

   4. distribute-rgt-neg-in 62.9%

      \[\leadsto \color{blue}{\log \left(1 - 4 \cdot u\right) \cdot \left(-s\right)} \]

   5. sub-neg 62.9%

      \[\leadsto \log \color{blue}{\left(1 + \left(-4 \cdot u\right)\right)} \cdot \left(-s\right) \]

   6. log1p-def 99.4%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(-4 \cdot u\right)} \cdot \left(-s\right) \]

   7. *-commutative 99.4%

      \[\leadsto \mathsf{log1p}\left(-\color{blue}{u \cdot 4}\right) \cdot \left(-s\right) \]

   8. distribute-rgt-neg-in 99.4%

      \[\leadsto \mathsf{log1p}\left(\color{blue}{u \cdot \left(-4\right)}\right) \cdot \left(-s\right) \]

   9. metadata-eval 99.4%

      \[\leadsto \mathsf{log1p}\left(u \cdot \color{blue}{-4}\right) \cdot \left(-s\right) \]

3. Simplified 99.4%

   \[\leadsto \color{blue}{\mathsf{log1p}\left(u \cdot -4\right) \cdot \left(-s\right)} \]

4. Final simplification 99.4%

   \[\leadsto \mathsf{log1p}\left(u \cdot -4\right) \cdot \left(-s\right) \]

Alternative 2: 86.7% accurate, 8.4× speedup

Math

\[\begin{array}{l} \\ u \cdot \left(s \cdot 4\right) + u \cdot \left(u \cdot \left(s \cdot 8\right)\right) \end{array} \]

FPCore

(FPCore (s u) :precision binary32 (+ (* u (* s 4.0)) (* u (* u (* s 8.0)))))

C

float code(float s, float u) {
	return (u * (s * 4.0f)) + (u * (u * (s * 8.0f)));
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = (u * (s * 4.0e0)) + (u * (u * (s * 8.0e0)))
end function

Julia

function code(s, u)
	return Float32(Float32(u * Float32(s * Float32(4.0))) + Float32(u * Float32(u * Float32(s * Float32(8.0)))))
end

MATLAB

function tmp = code(s, u)
	tmp = (u * (s * single(4.0))) + (u * (u * (s * single(8.0))));
end

Derivation

1. Initial program 59.7%

   \[s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right) \]
2. Step-by-step derivation

   1. flip3-- 58.8%

      \[\leadsto s \cdot \log \left(\frac{1}{\color{blue}{\frac{{1}^{3} - {\left(4 \cdot u\right)}^{3}}{1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)}}}\right) \]

   2. associate-/r/ 58.9%

      \[\leadsto s \cdot \log \color{blue}{\left(\frac{1}{{1}^{3} - {\left(4 \cdot u\right)}^{3}} \cdot \left(1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right)} \]

   3. log-prod 58.8%

      \[\leadsto s \cdot \color{blue}{\left(\log \left(\frac{1}{{1}^{3} - {\left(4 \cdot u\right)}^{3}}\right) + \log \left(1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right)} \]

   4. metadata-eval 58.8%

      \[\leadsto s \cdot \left(\log \left(\frac{1}{\color{blue}{1} - {\left(4 \cdot u\right)}^{3}}\right) + \log \left(1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right) \]

   5. *-commutative 58.8%

      \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {\color{blue}{\left(u \cdot 4\right)}}^{3}}\right) + \log \left(1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right) \]

   6. unpow-prod-down 58.8%

      \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - \color{blue}{{u}^{3} \cdot {4}^{3}}}\right) + \log \left(1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right) \]

   7. metadata-eval 58.8%

      \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {u}^{3} \cdot \color{blue}{64}}\right) + \log \left(1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right) \]

   8. metadata-eval 58.8%

      \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right) + \log \left(\color{blue}{1} + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right) \]

   9. log1p-udef 96.0%

      \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right) + \color{blue}{\mathsf{log1p}\left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)}\right) \]

   10. *-un-lft-identity 96.0%

       \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right) + \mathsf{log1p}\left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + \color{blue}{4 \cdot u}\right)\right) \]

   11. distribute-lft1-in 96.0%

       \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right) + \mathsf{log1p}\left(\color{blue}{\left(4 \cdot u + 1\right) \cdot \left(4 \cdot u\right)}\right)\right) \]

   12. fma-def 96.0%

       \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right) + \mathsf{log1p}\left(\color{blue}{\mathsf{fma}\left(4, u, 1\right)} \cdot \left(4 \cdot u\right)\right)\right) \]

3. Applied egg-rr 96.0%

   \[\leadsto s \cdot \color{blue}{\left(\log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right) + \mathsf{log1p}\left(\mathsf{fma}\left(4, u, 1\right) \cdot \left(4 \cdot u\right)\right)\right)} \]
4. Step-by-step derivation

   1. +-commutative 96.0%

      \[\leadsto s \cdot \color{blue}{\left(\mathsf{log1p}\left(\mathsf{fma}\left(4, u, 1\right) \cdot \left(4 \cdot u\right)\right) + \log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right)\right)} \]

   2. log-rec 96.7%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\mathsf{fma}\left(4, u, 1\right) \cdot \left(4 \cdot u\right)\right) + \color{blue}{\left(-\log \left(1 - {u}^{3} \cdot 64\right)\right)}\right) \]

   3. sub-neg 96.7%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\mathsf{fma}\left(4, u, 1\right) \cdot \left(4 \cdot u\right)\right) + \left(-\log \color{blue}{\left(1 + \left(-{u}^{3} \cdot 64\right)\right)}\right)\right) \]

   4. log1p-def 99.1%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\mathsf{fma}\left(4, u, 1\right) \cdot \left(4 \cdot u\right)\right) + \left(-\color{blue}{\mathsf{log1p}\left(-{u}^{3} \cdot 64\right)}\right)\right) \]

   5. unsub-neg 99.1%

      \[\leadsto s \cdot \color{blue}{\left(\mathsf{log1p}\left(\mathsf{fma}\left(4, u, 1\right) \cdot \left(4 \cdot u\right)\right) - \mathsf{log1p}\left(-{u}^{3} \cdot 64\right)\right)} \]

   6. *-commutative 99.1%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\color{blue}{\left(4 \cdot u\right) \cdot \mathsf{fma}\left(4, u, 1\right)}\right) - \mathsf{log1p}\left(-{u}^{3} \cdot 64\right)\right) \]

   7. *-commutative 99.1%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\color{blue}{\left(u \cdot 4\right)} \cdot \mathsf{fma}\left(4, u, 1\right)\right) - \mathsf{log1p}\left(-{u}^{3} \cdot 64\right)\right) \]

   8. associate-*l* 99.1%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\color{blue}{u \cdot \left(4 \cdot \mathsf{fma}\left(4, u, 1\right)\right)}\right) - \mathsf{log1p}\left(-{u}^{3} \cdot 64\right)\right) \]

   9. distribute-rgt-neg-in 99.1%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(u \cdot \left(4 \cdot \mathsf{fma}\left(4, u, 1\right)\right)\right) - \mathsf{log1p}\left(\color{blue}{{u}^{3} \cdot \left(-64\right)}\right)\right) \]

   10. metadata-eval 99.1%

       \[\leadsto s \cdot \left(\mathsf{log1p}\left(u \cdot \left(4 \cdot \mathsf{fma}\left(4, u, 1\right)\right)\right) - \mathsf{log1p}\left({u}^{3} \cdot \color{blue}{-64}\right)\right) \]

5. Simplified 99.1%

   \[\leadsto s \cdot \color{blue}{\left(\mathsf{log1p}\left(u \cdot \left(4 \cdot \mathsf{fma}\left(4, u, 1\right)\right)\right) - \mathsf{log1p}\left({u}^{3} \cdot -64\right)\right)} \]

6. Taylor expanded in u around 0 86.5%

   \[\leadsto \color{blue}{4 \cdot \left(s \cdot u\right) + 8 \cdot \left(s \cdot {u}^{2}\right)} \]
7. Step-by-step derivation

   1. associate-*r* 86.7%

      \[\leadsto \color{blue}{\left(4 \cdot s\right) \cdot u} + 8 \cdot \left(s \cdot {u}^{2}\right) \]

   2. unpow2 86.7%

      \[\leadsto \left(4 \cdot s\right) \cdot u + 8 \cdot \left(s \cdot \color{blue}{\left(u \cdot u\right)}\right) \]

   3. fma-def 87.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(4 \cdot s, u, 8 \cdot \left(s \cdot \left(u \cdot u\right)\right)\right)} \]

   4. unpow2 87.0%

      \[\leadsto \mathsf{fma}\left(4 \cdot s, u, 8 \cdot \left(s \cdot \color{blue}{{u}^{2}}\right)\right) \]

   5. associate-*r* 87.1%

      \[\leadsto \mathsf{fma}\left(4 \cdot s, u, \color{blue}{\left(8 \cdot s\right) \cdot {u}^{2}}\right) \]

   6. unpow2 87.1%

      \[\leadsto \mathsf{fma}\left(4 \cdot s, u, \left(8 \cdot s\right) \cdot \color{blue}{\left(u \cdot u\right)}\right) \]

   7. associate-*r* 87.1%

      \[\leadsto \mathsf{fma}\left(4 \cdot s, u, \color{blue}{\left(\left(8 \cdot s\right) \cdot u\right) \cdot u}\right) \]

8. Simplified 87.1%

   \[\leadsto \color{blue}{\mathsf{fma}\left(4 \cdot s, u, \left(\left(8 \cdot s\right) \cdot u\right) \cdot u\right)} \]
9. Step-by-step derivation

   1. fma-udef 86.9%

      \[\leadsto \color{blue}{\left(4 \cdot s\right) \cdot u + \left(\left(8 \cdot s\right) \cdot u\right) \cdot u} \]

   2. *-commutative 86.9%

      \[\leadsto \color{blue}{\left(s \cdot 4\right)} \cdot u + \left(\left(8 \cdot s\right) \cdot u\right) \cdot u \]

   3. *-commutative 86.9%

      \[\leadsto \left(s \cdot 4\right) \cdot u + \color{blue}{u \cdot \left(\left(8 \cdot s\right) \cdot u\right)} \]

   4. *-commutative 86.9%

      \[\leadsto \left(s \cdot 4\right) \cdot u + u \cdot \color{blue}{\left(u \cdot \left(8 \cdot s\right)\right)} \]

   5. *-commutative 86.9%

      \[\leadsto \left(s \cdot 4\right) \cdot u + u \cdot \left(u \cdot \color{blue}{\left(s \cdot 8\right)}\right) \]

10. Applied egg-rr 86.9%

    \[\leadsto \color{blue}{\left(s \cdot 4\right) \cdot u + u \cdot \left(u \cdot \left(s \cdot 8\right)\right)} \]

11. Final simplification 86.9%

    \[\leadsto u \cdot \left(s \cdot 4\right) + u \cdot \left(u \cdot \left(s \cdot 8\right)\right) \]

Alternative 3: 86.7% accurate, 9.9× speedup

Math

\[\begin{array}{l} \\ s \cdot \left(8 \cdot \left(u \cdot u\right) + u \cdot 4\right) \end{array} \]

FPCore

(FPCore (s u) :precision binary32 (* s (+ (* 8.0 (* u u)) (* u 4.0))))

C

float code(float s, float u) {
	return s * ((8.0f * (u * u)) + (u * 4.0f));
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = s * ((8.0e0 * (u * u)) + (u * 4.0e0))
end function

Julia

function code(s, u)
	return Float32(s * Float32(Float32(Float32(8.0) * Float32(u * u)) + Float32(u * Float32(4.0))))
end

MATLAB

function tmp = code(s, u)
	tmp = s * ((single(8.0) * (u * u)) + (u * single(4.0)));
end

Derivation

1. Initial program 59.7%

   \[s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right) \]

2. Taylor expanded in u around 0 86.8%

   \[\leadsto s \cdot \color{blue}{\left(8 \cdot {u}^{2} + 4 \cdot u\right)} \]
3. Step-by-step derivation

   1. fma-def 86.8%

      \[\leadsto s \cdot \color{blue}{\mathsf{fma}\left(8, {u}^{2}, 4 \cdot u\right)} \]

   2. unpow2 86.8%

      \[\leadsto s \cdot \mathsf{fma}\left(8, \color{blue}{u \cdot u}, 4 \cdot u\right) \]

4. Simplified 86.8%

   \[\leadsto s \cdot \color{blue}{\mathsf{fma}\left(8, u \cdot u, 4 \cdot u\right)} \]
5. Step-by-step derivation

   1. fma-udef 86.8%

      \[\leadsto s \cdot \color{blue}{\left(8 \cdot \left(u \cdot u\right) + 4 \cdot u\right)} \]

   2. *-commutative 86.8%

      \[\leadsto s \cdot \left(8 \cdot \left(u \cdot u\right) + \color{blue}{u \cdot 4}\right) \]

6. Applied egg-rr 86.8%

   \[\leadsto s \cdot \color{blue}{\left(8 \cdot \left(u \cdot u\right) + u \cdot 4\right)} \]

7. Final simplification 86.8%

   \[\leadsto s \cdot \left(8 \cdot \left(u \cdot u\right) + u \cdot 4\right) \]

Alternative 4: 86.6% accurate, 12.1× speedup

Math

\[\begin{array}{l} \\ s \cdot \left(u \cdot \left(4 + u \cdot 8\right)\right) \end{array} \]

FPCore

(FPCore (s u) :precision binary32 (* s (* u (+ 4.0 (* u 8.0)))))

C

float code(float s, float u) {
	return s * (u * (4.0f + (u * 8.0f)));
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = s * (u * (4.0e0 + (u * 8.0e0)))
end function

Julia

function code(s, u)
	return Float32(s * Float32(u * Float32(Float32(4.0) + Float32(u * Float32(8.0)))))
end

MATLAB

function tmp = code(s, u)
	tmp = s * (u * (single(4.0) + (u * single(8.0))));
end

Derivation

1. Initial program 59.7%

   \[s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right) \]
2. Step-by-step derivation

   1. flip3-- 58.8%

      \[\leadsto s \cdot \log \left(\frac{1}{\color{blue}{\frac{{1}^{3} - {\left(4 \cdot u\right)}^{3}}{1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)}}}\right) \]

   2. associate-/r/ 58.9%

      \[\leadsto s \cdot \log \color{blue}{\left(\frac{1}{{1}^{3} - {\left(4 \cdot u\right)}^{3}} \cdot \left(1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right)} \]

   3. log-prod 58.8%

      \[\leadsto s \cdot \color{blue}{\left(\log \left(\frac{1}{{1}^{3} - {\left(4 \cdot u\right)}^{3}}\right) + \log \left(1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right)} \]

   4. metadata-eval 58.8%

      \[\leadsto s \cdot \left(\log \left(\frac{1}{\color{blue}{1} - {\left(4 \cdot u\right)}^{3}}\right) + \log \left(1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right) \]

   5. *-commutative 58.8%

      \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {\color{blue}{\left(u \cdot 4\right)}}^{3}}\right) + \log \left(1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right) \]

   6. unpow-prod-down 58.8%

      \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - \color{blue}{{u}^{3} \cdot {4}^{3}}}\right) + \log \left(1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right) \]

   7. metadata-eval 58.8%

      \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {u}^{3} \cdot \color{blue}{64}}\right) + \log \left(1 \cdot 1 + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right) \]

   8. metadata-eval 58.8%

      \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right) + \log \left(\color{blue}{1} + \left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)\right)\right) \]

   9. log1p-udef 96.0%

      \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right) + \color{blue}{\mathsf{log1p}\left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + 1 \cdot \left(4 \cdot u\right)\right)}\right) \]

   10. *-un-lft-identity 96.0%

       \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right) + \mathsf{log1p}\left(\left(4 \cdot u\right) \cdot \left(4 \cdot u\right) + \color{blue}{4 \cdot u}\right)\right) \]

   11. distribute-lft1-in 96.0%

       \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right) + \mathsf{log1p}\left(\color{blue}{\left(4 \cdot u + 1\right) \cdot \left(4 \cdot u\right)}\right)\right) \]

   12. fma-def 96.0%

       \[\leadsto s \cdot \left(\log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right) + \mathsf{log1p}\left(\color{blue}{\mathsf{fma}\left(4, u, 1\right)} \cdot \left(4 \cdot u\right)\right)\right) \]

3. Applied egg-rr 96.0%

   \[\leadsto s \cdot \color{blue}{\left(\log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right) + \mathsf{log1p}\left(\mathsf{fma}\left(4, u, 1\right) \cdot \left(4 \cdot u\right)\right)\right)} \]
4. Step-by-step derivation

   1. +-commutative 96.0%

      \[\leadsto s \cdot \color{blue}{\left(\mathsf{log1p}\left(\mathsf{fma}\left(4, u, 1\right) \cdot \left(4 \cdot u\right)\right) + \log \left(\frac{1}{1 - {u}^{3} \cdot 64}\right)\right)} \]

   2. log-rec 96.7%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\mathsf{fma}\left(4, u, 1\right) \cdot \left(4 \cdot u\right)\right) + \color{blue}{\left(-\log \left(1 - {u}^{3} \cdot 64\right)\right)}\right) \]

   3. sub-neg 96.7%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\mathsf{fma}\left(4, u, 1\right) \cdot \left(4 \cdot u\right)\right) + \left(-\log \color{blue}{\left(1 + \left(-{u}^{3} \cdot 64\right)\right)}\right)\right) \]

   4. log1p-def 99.1%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\mathsf{fma}\left(4, u, 1\right) \cdot \left(4 \cdot u\right)\right) + \left(-\color{blue}{\mathsf{log1p}\left(-{u}^{3} \cdot 64\right)}\right)\right) \]

   5. unsub-neg 99.1%

      \[\leadsto s \cdot \color{blue}{\left(\mathsf{log1p}\left(\mathsf{fma}\left(4, u, 1\right) \cdot \left(4 \cdot u\right)\right) - \mathsf{log1p}\left(-{u}^{3} \cdot 64\right)\right)} \]

   6. *-commutative 99.1%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\color{blue}{\left(4 \cdot u\right) \cdot \mathsf{fma}\left(4, u, 1\right)}\right) - \mathsf{log1p}\left(-{u}^{3} \cdot 64\right)\right) \]

   7. *-commutative 99.1%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\color{blue}{\left(u \cdot 4\right)} \cdot \mathsf{fma}\left(4, u, 1\right)\right) - \mathsf{log1p}\left(-{u}^{3} \cdot 64\right)\right) \]

   8. associate-*l* 99.1%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\color{blue}{u \cdot \left(4 \cdot \mathsf{fma}\left(4, u, 1\right)\right)}\right) - \mathsf{log1p}\left(-{u}^{3} \cdot 64\right)\right) \]

   9. distribute-rgt-neg-in 99.1%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(u \cdot \left(4 \cdot \mathsf{fma}\left(4, u, 1\right)\right)\right) - \mathsf{log1p}\left(\color{blue}{{u}^{3} \cdot \left(-64\right)}\right)\right) \]

   10. metadata-eval 99.1%

       \[\leadsto s \cdot \left(\mathsf{log1p}\left(u \cdot \left(4 \cdot \mathsf{fma}\left(4, u, 1\right)\right)\right) - \mathsf{log1p}\left({u}^{3} \cdot \color{blue}{-64}\right)\right) \]

5. Simplified 99.1%

   \[\leadsto s \cdot \color{blue}{\left(\mathsf{log1p}\left(u \cdot \left(4 \cdot \mathsf{fma}\left(4, u, 1\right)\right)\right) - \mathsf{log1p}\left({u}^{3} \cdot -64\right)\right)} \]

6. Taylor expanded in u around 0 99.1%

   \[\leadsto s \cdot \left(\mathsf{log1p}\left(\color{blue}{16 \cdot {u}^{2} + 4 \cdot u}\right) - \mathsf{log1p}\left({u}^{3} \cdot -64\right)\right) \]
7. Step-by-step derivation

   1. fma-def 99.1%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\color{blue}{\mathsf{fma}\left(16, {u}^{2}, 4 \cdot u\right)}\right) - \mathsf{log1p}\left({u}^{3} \cdot -64\right)\right) \]

   2. unpow2 99.1%

      \[\leadsto s \cdot \left(\mathsf{log1p}\left(\mathsf{fma}\left(16, \color{blue}{u \cdot u}, 4 \cdot u\right)\right) - \mathsf{log1p}\left({u}^{3} \cdot -64\right)\right) \]

8. Simplified 99.1%

   \[\leadsto s \cdot \left(\mathsf{log1p}\left(\color{blue}{\mathsf{fma}\left(16, u \cdot u, 4 \cdot u\right)}\right) - \mathsf{log1p}\left({u}^{3} \cdot -64\right)\right) \]

9. Taylor expanded in u around 0 86.5%

   \[\leadsto \color{blue}{4 \cdot \left(s \cdot u\right) + 8 \cdot \left(s \cdot {u}^{2}\right)} \]
10. Step-by-step derivation

    1. *-commutative 86.5%

       \[\leadsto 4 \cdot \color{blue}{\left(u \cdot s\right)} + 8 \cdot \left(s \cdot {u}^{2}\right) \]

    2. associate-*r* 86.7%

       \[\leadsto \color{blue}{\left(4 \cdot u\right) \cdot s} + 8 \cdot \left(s \cdot {u}^{2}\right) \]

    3. *-commutative 86.7%

       \[\leadsto \left(4 \cdot u\right) \cdot s + 8 \cdot \color{blue}{\left({u}^{2} \cdot s\right)} \]

    4. associate-*r* 86.9%

       \[\leadsto \left(4 \cdot u\right) \cdot s + \color{blue}{\left(8 \cdot {u}^{2}\right) \cdot s} \]

    5. distribute-rgt-in 86.8%

       \[\leadsto \color{blue}{s \cdot \left(4 \cdot u + 8 \cdot {u}^{2}\right)} \]

    6. +-commutative 86.8%

       \[\leadsto s \cdot \color{blue}{\left(8 \cdot {u}^{2} + 4 \cdot u\right)} \]

    7. +-commutative 86.8%

       \[\leadsto s \cdot \color{blue}{\left(4 \cdot u + 8 \cdot {u}^{2}\right)} \]

    8. unpow2 86.8%

       \[\leadsto s \cdot \left(4 \cdot u + 8 \cdot \color{blue}{\left(u \cdot u\right)}\right) \]

    9. associate-*r* 86.8%

       \[\leadsto s \cdot \left(4 \cdot u + \color{blue}{\left(8 \cdot u\right) \cdot u}\right) \]

    10. distribute-rgt-out 86.6%

        \[\leadsto s \cdot \color{blue}{\left(u \cdot \left(4 + 8 \cdot u\right)\right)} \]

    11. *-commutative 86.6%

        \[\leadsto s \cdot \left(u \cdot \left(4 + \color{blue}{u \cdot 8}\right)\right) \]

11. Simplified 86.6%

    \[\leadsto \color{blue}{s \cdot \left(u \cdot \left(4 + u \cdot 8\right)\right)} \]

12. Final simplification 86.6%

    \[\leadsto s \cdot \left(u \cdot \left(4 + u \cdot 8\right)\right) \]

Alternative 5: 73.4% accurate, 21.8× speedup

Math

\[\begin{array}{l} \\ 4 \cdot \left(u \cdot s\right) \end{array} \]

FPCore

(FPCore (s u) :precision binary32 (* 4.0 (* u s)))

C

float code(float s, float u) {
	return 4.0f * (u * s);
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = 4.0e0 * (u * s)
end function

Julia

function code(s, u)
	return Float32(Float32(4.0) * Float32(u * s))
end

MATLAB

function tmp = code(s, u)
	tmp = single(4.0) * (u * s);
end

Derivation

1. Initial program 59.7%

   \[s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right) \]

2. Taylor expanded in u around 0 74.2%

   \[\leadsto \color{blue}{4 \cdot \left(s \cdot u\right)} \]
3. Step-by-step derivation

   1. *-commutative 74.2%

      \[\leadsto 4 \cdot \color{blue}{\left(u \cdot s\right)} \]

4. Simplified 74.2%

   \[\leadsto \color{blue}{4 \cdot \left(u \cdot s\right)} \]

5. Final simplification 74.2%

   \[\leadsto 4 \cdot \left(u \cdot s\right) \]

Alternative 6: 73.6% accurate, 21.8× speedup

Math

\[\begin{array}{l} \\ s \cdot \left(u \cdot 4\right) \end{array} \]

FPCore

(FPCore (s u) :precision binary32 (* s (* u 4.0)))

C

float code(float s, float u) {
	return s * (u * 4.0f);
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = s * (u * 4.0e0)
end function

Julia

function code(s, u)
	return Float32(s * Float32(u * Float32(4.0)))
end

MATLAB

function tmp = code(s, u)
	tmp = s * (u * single(4.0));
end

Derivation

1. Initial program 59.7%

   \[s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right) \]

2. Taylor expanded in u around 0 74.4%

   \[\leadsto s \cdot \color{blue}{\left(4 \cdot u\right)} \]

3. Final simplification 74.4%

   \[\leadsto s \cdot \left(u \cdot 4\right) \]

Alternative 7: 16.5% accurate, 36.3× speedup

Math

\[\begin{array}{l} \\ s \cdot 0 \end{array} \]

FPCore

(FPCore (s u) :precision binary32 (* s 0.0))

C

float code(float s, float u) {
	return s * 0.0f;
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = s * 0.0e0
end function

Julia

function code(s, u)
	return Float32(s * Float32(0.0))
end

MATLAB

function tmp = code(s, u)
	tmp = s * single(0.0);
end

Derivation

1. Initial program 59.7%

   \[s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right) \]

3. Applied egg-rr 16.5%

   \[\leadsto s \cdot \color{blue}{\left(0.5 \cdot \mathsf{log1p}\left(4 \cdot u\right) - 0.5 \cdot \mathsf{log1p}\left(4 \cdot u\right)\right)} \]
4. Step-by-step derivation

   1. +-inverses 16.5%

      \[\leadsto s \cdot \color{blue}{0} \]

5. Simplified 16.5%

   \[\leadsto s \cdot \color{blue}{0} \]

6. Final simplification 16.5%

   \[\leadsto s \cdot 0 \]

Reproduce

herbie shell --seed 2023240 
(FPCore (s u)
  :name "Disney BSSRDF, sample scattering profile, lower"
  :precision binary32
  :pre (and (and (<= 0.0 s) (<= s 256.0)) (and (<= 2.328306437e-10 u) (<= u 0.25)))
  (* s (log (/ 1.0 (- 1.0 (* 4.0 u))))))