Disney BSSRDF, sample scattering profile, lower

Percentage Accurate: 61.4% → 99.4%

Time: 9.9s

Alternatives: 9

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: 61.4% 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

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

FPCore

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

C

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

Julia

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

Derivation

1. Initial program 59.0%

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

   1. log-rec 61.4%

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

   2. distribute-rgt-neg-out 61.4%

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

   3. distribute-lft-neg-out 61.4%

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

   4. cancel-sign-sub-inv 61.4%

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

   5. log1p-def 99.5%

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

   6. *-commutative 99.5%

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

   7. metadata-eval 99.5%

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

3. Simplified 99.5%

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

4. Final simplification 99.5%

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

Alternative 2: 90.9% accurate, 3.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := u \cdot \left(u \cdot 21.333333333333332\right)\\ s \cdot \left(u \cdot \left(4 + \frac{t_0 \cdot t_0 - \left(u \cdot 8\right) \cdot \left(u \cdot 8\right)}{t_0 - u \cdot 8}\right)\right) \end{array} \end{array} \]

FPCore

(FPCore (s u)
 :precision binary32
 (let* ((t_0 (* u (* u 21.333333333333332))))
   (*
    s
    (*
     u
     (+ 4.0 (/ (- (* t_0 t_0) (* (* u 8.0) (* u 8.0))) (- t_0 (* u 8.0))))))))

C

float code(float s, float u) {
	float t_0 = u * (u * 21.333333333333332f);
	return s * (u * (4.0f + (((t_0 * t_0) - ((u * 8.0f) * (u * 8.0f))) / (t_0 - (u * 8.0f)))));
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    real(4) :: t_0
    t_0 = u * (u * 21.333333333333332e0)
    code = s * (u * (4.0e0 + (((t_0 * t_0) - ((u * 8.0e0) * (u * 8.0e0))) / (t_0 - (u * 8.0e0)))))
end function

Julia

function code(s, u)
	t_0 = Float32(u * Float32(u * Float32(21.333333333333332)))
	return Float32(s * Float32(u * Float32(Float32(4.0) + Float32(Float32(Float32(t_0 * t_0) - Float32(Float32(u * Float32(8.0)) * Float32(u * Float32(8.0)))) / Float32(t_0 - Float32(u * Float32(8.0)))))))
end

MATLAB

function tmp = code(s, u)
	t_0 = u * (u * single(21.333333333333332));
	tmp = s * (u * (single(4.0) + (((t_0 * t_0) - ((u * single(8.0)) * (u * single(8.0)))) / (t_0 - (u * single(8.0))))));
end

Derivation

1. Initial program 59.0%

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

2. Taylor expanded in u around 0 92.6%

   \[\leadsto s \cdot \color{blue}{\left(4 \cdot u + \left(8 \cdot {u}^{2} + 21.333333333333332 \cdot {u}^{3}\right)\right)} \]
3. Step-by-step derivation

   1. *-commutative 92.6%

      \[\leadsto s \cdot \left(\color{blue}{u \cdot 4} + \left(8 \cdot {u}^{2} + 21.333333333333332 \cdot {u}^{3}\right)\right) \]

   2. +-commutative 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \color{blue}{\left(21.333333333333332 \cdot {u}^{3} + 8 \cdot {u}^{2}\right)}\right) \]

   3. *-commutative 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(\color{blue}{{u}^{3} \cdot 21.333333333333332} + 8 \cdot {u}^{2}\right)\right) \]

   4. cube-mult 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(\color{blue}{\left(u \cdot \left(u \cdot u\right)\right)} \cdot 21.333333333333332 + 8 \cdot {u}^{2}\right)\right) \]

   5. unpow2 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(\left(u \cdot \color{blue}{{u}^{2}}\right) \cdot 21.333333333333332 + 8 \cdot {u}^{2}\right)\right) \]

   6. associate-*l* 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(\color{blue}{u \cdot \left({u}^{2} \cdot 21.333333333333332\right)} + 8 \cdot {u}^{2}\right)\right) \]

   7. *-commutative 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(u \cdot \left({u}^{2} \cdot 21.333333333333332\right) + \color{blue}{{u}^{2} \cdot 8}\right)\right) \]

   8. unpow2 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(u \cdot \left({u}^{2} \cdot 21.333333333333332\right) + \color{blue}{\left(u \cdot u\right)} \cdot 8\right)\right) \]

   9. associate-*l* 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(u \cdot \left({u}^{2} \cdot 21.333333333333332\right) + \color{blue}{u \cdot \left(u \cdot 8\right)}\right)\right) \]

   10. distribute-lft-out 92.6%

       \[\leadsto s \cdot \left(u \cdot 4 + \color{blue}{u \cdot \left({u}^{2} \cdot 21.333333333333332 + u \cdot 8\right)}\right) \]

   11. distribute-lft-out 92.3%

       \[\leadsto s \cdot \color{blue}{\left(u \cdot \left(4 + \left({u}^{2} \cdot 21.333333333333332 + u \cdot 8\right)\right)\right)} \]

   12. unpow2 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + \left(\color{blue}{\left(u \cdot u\right)} \cdot 21.333333333333332 + u \cdot 8\right)\right)\right) \]

   13. associate-*l* 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + \left(\color{blue}{u \cdot \left(u \cdot 21.333333333333332\right)} + u \cdot 8\right)\right)\right) \]

   14. *-commutative 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + \left(u \cdot \color{blue}{\left(21.333333333333332 \cdot u\right)} + u \cdot 8\right)\right)\right) \]

   15. distribute-lft-out 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + \color{blue}{u \cdot \left(21.333333333333332 \cdot u + 8\right)}\right)\right) \]

   16. *-commutative 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + u \cdot \left(\color{blue}{u \cdot 21.333333333333332} + 8\right)\right)\right) \]

4. Simplified 92.3%

   \[\leadsto s \cdot \color{blue}{\left(u \cdot \left(4 + u \cdot \left(u \cdot 21.333333333333332 + 8\right)\right)\right)} \]
5. Step-by-step derivation

   1. distribute-rgt-in 92.3%

      \[\leadsto s \cdot \left(u \cdot \left(4 + \color{blue}{\left(\left(u \cdot 21.333333333333332\right) \cdot u + 8 \cdot u\right)}\right)\right) \]

   2. flip-+ 92.3%

      \[\leadsto s \cdot \left(u \cdot \left(4 + \color{blue}{\frac{\left(\left(u \cdot 21.333333333333332\right) \cdot u\right) \cdot \left(\left(u \cdot 21.333333333333332\right) \cdot u\right) - \left(8 \cdot u\right) \cdot \left(8 \cdot u\right)}{\left(u \cdot 21.333333333333332\right) \cdot u - 8 \cdot u}}\right)\right) \]

   3. *-commutative 92.3%

      \[\leadsto s \cdot \left(u \cdot \left(4 + \frac{\left(\color{blue}{\left(21.333333333333332 \cdot u\right)} \cdot u\right) \cdot \left(\left(u \cdot 21.333333333333332\right) \cdot u\right) - \left(8 \cdot u\right) \cdot \left(8 \cdot u\right)}{\left(u \cdot 21.333333333333332\right) \cdot u - 8 \cdot u}\right)\right) \]

   4. *-commutative 92.3%

      \[\leadsto s \cdot \left(u \cdot \left(4 + \frac{\left(\left(21.333333333333332 \cdot u\right) \cdot u\right) \cdot \left(\color{blue}{\left(21.333333333333332 \cdot u\right)} \cdot u\right) - \left(8 \cdot u\right) \cdot \left(8 \cdot u\right)}{\left(u \cdot 21.333333333333332\right) \cdot u - 8 \cdot u}\right)\right) \]

   5. *-commutative 92.3%

      \[\leadsto s \cdot \left(u \cdot \left(4 + \frac{\left(\left(21.333333333333332 \cdot u\right) \cdot u\right) \cdot \left(\left(21.333333333333332 \cdot u\right) \cdot u\right) - \left(8 \cdot u\right) \cdot \left(8 \cdot u\right)}{\color{blue}{\left(21.333333333333332 \cdot u\right)} \cdot u - 8 \cdot u}\right)\right) \]

6. Applied egg-rr 92.3%

   \[\leadsto s \cdot \left(u \cdot \left(4 + \color{blue}{\frac{\left(\left(21.333333333333332 \cdot u\right) \cdot u\right) \cdot \left(\left(21.333333333333332 \cdot u\right) \cdot u\right) - \left(8 \cdot u\right) \cdot \left(8 \cdot u\right)}{\left(21.333333333333332 \cdot u\right) \cdot u - 8 \cdot u}}\right)\right) \]

7. Final simplification 92.3%

   \[\leadsto s \cdot \left(u \cdot \left(4 + \frac{\left(u \cdot \left(u \cdot 21.333333333333332\right)\right) \cdot \left(u \cdot \left(u \cdot 21.333333333333332\right)\right) - \left(u \cdot 8\right) \cdot \left(u \cdot 8\right)}{u \cdot \left(u \cdot 21.333333333333332\right) - u \cdot 8}\right)\right) \]

Alternative 3: 90.9% accurate, 7.3× speedup

Math

\[\begin{array}{l} \\ s \cdot \left(u \cdot \left(4 + \left(u \cdot \left(u \cdot 21.333333333333332\right) + u \cdot 8\right)\right)\right) \end{array} \]

FPCore

(FPCore (s u)
 :precision binary32
 (* s (* u (+ 4.0 (+ (* u (* u 21.333333333333332)) (* u 8.0))))))

C

float code(float s, float u) {
	return s * (u * (4.0f + ((u * (u * 21.333333333333332f)) + (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 * (u * 21.333333333333332e0)) + (u * 8.0e0))))
end function

Julia

function code(s, u)
	return Float32(s * Float32(u * Float32(Float32(4.0) + Float32(Float32(u * Float32(u * Float32(21.333333333333332))) + Float32(u * Float32(8.0))))))
end

MATLAB

function tmp = code(s, u)
	tmp = s * (u * (single(4.0) + ((u * (u * single(21.333333333333332))) + (u * single(8.0)))));
end

Derivation

1. Initial program 59.0%

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

2. Taylor expanded in u around 0 92.6%

   \[\leadsto s \cdot \color{blue}{\left(4 \cdot u + \left(8 \cdot {u}^{2} + 21.333333333333332 \cdot {u}^{3}\right)\right)} \]
3. Step-by-step derivation

   1. *-commutative 92.6%

      \[\leadsto s \cdot \left(\color{blue}{u \cdot 4} + \left(8 \cdot {u}^{2} + 21.333333333333332 \cdot {u}^{3}\right)\right) \]

   2. +-commutative 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \color{blue}{\left(21.333333333333332 \cdot {u}^{3} + 8 \cdot {u}^{2}\right)}\right) \]

   3. *-commutative 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(\color{blue}{{u}^{3} \cdot 21.333333333333332} + 8 \cdot {u}^{2}\right)\right) \]

   4. cube-mult 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(\color{blue}{\left(u \cdot \left(u \cdot u\right)\right)} \cdot 21.333333333333332 + 8 \cdot {u}^{2}\right)\right) \]

   5. unpow2 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(\left(u \cdot \color{blue}{{u}^{2}}\right) \cdot 21.333333333333332 + 8 \cdot {u}^{2}\right)\right) \]

   6. associate-*l* 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(\color{blue}{u \cdot \left({u}^{2} \cdot 21.333333333333332\right)} + 8 \cdot {u}^{2}\right)\right) \]

   7. *-commutative 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(u \cdot \left({u}^{2} \cdot 21.333333333333332\right) + \color{blue}{{u}^{2} \cdot 8}\right)\right) \]

   8. unpow2 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(u \cdot \left({u}^{2} \cdot 21.333333333333332\right) + \color{blue}{\left(u \cdot u\right)} \cdot 8\right)\right) \]

   9. associate-*l* 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(u \cdot \left({u}^{2} \cdot 21.333333333333332\right) + \color{blue}{u \cdot \left(u \cdot 8\right)}\right)\right) \]

   10. distribute-lft-out 92.6%

       \[\leadsto s \cdot \left(u \cdot 4 + \color{blue}{u \cdot \left({u}^{2} \cdot 21.333333333333332 + u \cdot 8\right)}\right) \]

   11. distribute-lft-out 92.3%

       \[\leadsto s \cdot \color{blue}{\left(u \cdot \left(4 + \left({u}^{2} \cdot 21.333333333333332 + u \cdot 8\right)\right)\right)} \]

   12. unpow2 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + \left(\color{blue}{\left(u \cdot u\right)} \cdot 21.333333333333332 + u \cdot 8\right)\right)\right) \]

   13. associate-*l* 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + \left(\color{blue}{u \cdot \left(u \cdot 21.333333333333332\right)} + u \cdot 8\right)\right)\right) \]

   14. *-commutative 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + \left(u \cdot \color{blue}{\left(21.333333333333332 \cdot u\right)} + u \cdot 8\right)\right)\right) \]

   15. distribute-lft-out 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + \color{blue}{u \cdot \left(21.333333333333332 \cdot u + 8\right)}\right)\right) \]

   16. *-commutative 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + u \cdot \left(\color{blue}{u \cdot 21.333333333333332} + 8\right)\right)\right) \]

4. Simplified 92.3%

   \[\leadsto s \cdot \color{blue}{\left(u \cdot \left(4 + u \cdot \left(u \cdot 21.333333333333332 + 8\right)\right)\right)} \]
5. Step-by-step derivation

   1. distribute-rgt-in 92.3%

      \[\leadsto s \cdot \left(u \cdot \left(4 + \color{blue}{\left(\left(u \cdot 21.333333333333332\right) \cdot u + 8 \cdot u\right)}\right)\right) \]

   2. *-commutative 92.3%

      \[\leadsto s \cdot \left(u \cdot \left(4 + \left(\color{blue}{\left(21.333333333333332 \cdot u\right)} \cdot u + 8 \cdot u\right)\right)\right) \]

6. Applied egg-rr 92.3%

   \[\leadsto s \cdot \left(u \cdot \left(4 + \color{blue}{\left(\left(21.333333333333332 \cdot u\right) \cdot u + 8 \cdot u\right)}\right)\right) \]

7. Final simplification 92.3%

   \[\leadsto s \cdot \left(u \cdot \left(4 + \left(u \cdot \left(u \cdot 21.333333333333332\right) + u \cdot 8\right)\right)\right) \]

Alternative 4: 90.9% accurate, 8.4× speedup

Math

\[\begin{array}{l} \\ s \cdot \left(u \cdot \left(4 + u \cdot \left(8 + u \cdot 21.333333333333332\right)\right)\right) \end{array} \]

FPCore

(FPCore (s u)
 :precision binary32
 (* s (* u (+ 4.0 (* u (+ 8.0 (* u 21.333333333333332)))))))

C

float code(float s, float u) {
	return s * (u * (4.0f + (u * (8.0f + (u * 21.333333333333332f)))));
}

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 + (u * 21.333333333333332e0)))))
end function

Julia

function code(s, u)
	return Float32(s * Float32(u * Float32(Float32(4.0) + Float32(u * Float32(Float32(8.0) + Float32(u * Float32(21.333333333333332)))))))
end

MATLAB

function tmp = code(s, u)
	tmp = s * (u * (single(4.0) + (u * (single(8.0) + (u * single(21.333333333333332))))));
end

Derivation

1. Initial program 59.0%

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

2. Taylor expanded in u around 0 92.6%

   \[\leadsto s \cdot \color{blue}{\left(4 \cdot u + \left(8 \cdot {u}^{2} + 21.333333333333332 \cdot {u}^{3}\right)\right)} \]
3. Step-by-step derivation

   1. *-commutative 92.6%

      \[\leadsto s \cdot \left(\color{blue}{u \cdot 4} + \left(8 \cdot {u}^{2} + 21.333333333333332 \cdot {u}^{3}\right)\right) \]

   2. +-commutative 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \color{blue}{\left(21.333333333333332 \cdot {u}^{3} + 8 \cdot {u}^{2}\right)}\right) \]

   3. *-commutative 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(\color{blue}{{u}^{3} \cdot 21.333333333333332} + 8 \cdot {u}^{2}\right)\right) \]

   4. cube-mult 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(\color{blue}{\left(u \cdot \left(u \cdot u\right)\right)} \cdot 21.333333333333332 + 8 \cdot {u}^{2}\right)\right) \]

   5. unpow2 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(\left(u \cdot \color{blue}{{u}^{2}}\right) \cdot 21.333333333333332 + 8 \cdot {u}^{2}\right)\right) \]

   6. associate-*l* 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(\color{blue}{u \cdot \left({u}^{2} \cdot 21.333333333333332\right)} + 8 \cdot {u}^{2}\right)\right) \]

   7. *-commutative 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(u \cdot \left({u}^{2} \cdot 21.333333333333332\right) + \color{blue}{{u}^{2} \cdot 8}\right)\right) \]

   8. unpow2 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(u \cdot \left({u}^{2} \cdot 21.333333333333332\right) + \color{blue}{\left(u \cdot u\right)} \cdot 8\right)\right) \]

   9. associate-*l* 92.6%

      \[\leadsto s \cdot \left(u \cdot 4 + \left(u \cdot \left({u}^{2} \cdot 21.333333333333332\right) + \color{blue}{u \cdot \left(u \cdot 8\right)}\right)\right) \]

   10. distribute-lft-out 92.6%

       \[\leadsto s \cdot \left(u \cdot 4 + \color{blue}{u \cdot \left({u}^{2} \cdot 21.333333333333332 + u \cdot 8\right)}\right) \]

   11. distribute-lft-out 92.3%

       \[\leadsto s \cdot \color{blue}{\left(u \cdot \left(4 + \left({u}^{2} \cdot 21.333333333333332 + u \cdot 8\right)\right)\right)} \]

   12. unpow2 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + \left(\color{blue}{\left(u \cdot u\right)} \cdot 21.333333333333332 + u \cdot 8\right)\right)\right) \]

   13. associate-*l* 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + \left(\color{blue}{u \cdot \left(u \cdot 21.333333333333332\right)} + u \cdot 8\right)\right)\right) \]

   14. *-commutative 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + \left(u \cdot \color{blue}{\left(21.333333333333332 \cdot u\right)} + u \cdot 8\right)\right)\right) \]

   15. distribute-lft-out 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + \color{blue}{u \cdot \left(21.333333333333332 \cdot u + 8\right)}\right)\right) \]

   16. *-commutative 92.3%

       \[\leadsto s \cdot \left(u \cdot \left(4 + u \cdot \left(\color{blue}{u \cdot 21.333333333333332} + 8\right)\right)\right) \]

4. Simplified 92.3%

   \[\leadsto s \cdot \color{blue}{\left(u \cdot \left(4 + u \cdot \left(u \cdot 21.333333333333332 + 8\right)\right)\right)} \]

5. Final simplification 92.3%

   \[\leadsto s \cdot \left(u \cdot \left(4 + u \cdot \left(8 + u \cdot 21.333333333333332\right)\right)\right) \]

Alternative 5: 87.0% accurate, 9.9× speedup

Math

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

FPCore

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

C

float code(float s, float u) {
	return s * ((u * 4.0f) + (u * (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 * (u * 8.0e0)))
end function

Julia

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

MATLAB

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

Derivation

1. Initial program 59.0%

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

2. Taylor expanded in u around 0 88.1%

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

   1. unpow2 88.1%

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

   2. associate-*r* 88.1%

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

   3. distribute-rgt-out 88.0%

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

   4. *-commutative 88.0%

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

4. Simplified 88.0%

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

   1. distribute-rgt-in 88.1%

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

   2. *-commutative 88.1%

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

6. Applied egg-rr 88.1%

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

7. Final simplification 88.1%

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

Alternative 6: 86.8% 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 59.0%

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

2. Taylor expanded in u around 0 88.1%

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

   1. unpow2 88.1%

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

   2. associate-*r* 88.1%

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

   3. distribute-rgt-out 88.0%

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

   4. *-commutative 88.0%

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

4. Simplified 88.0%

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

5. Final simplification 88.0%

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

Alternative 7: 73.8% accurate, 21.8× speedup

Math

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

FPCore

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

C

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

Fortran

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

Julia

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

MATLAB

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

Derivation

1. Initial program 59.0%

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

2. Taylor expanded in u around 0 74.7%

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

   1. *-commutative 74.7%

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

4. Simplified 74.7%

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

5. Final simplification 74.7%

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

Alternative 8: 74.1% 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 59.0%

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

2. Taylor expanded in u around 0 74.9%

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

3. Final simplification 74.9%

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

Alternative 9: 17.0% 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 59.0%

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

3. Applied egg-rr 15.8%

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

4. Final simplification 15.8%

   \[\leadsto s \cdot 0 \]

Reproduce

herbie shell --seed 2023318 
(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))))))