Disney BSSRDF, sample scattering profile, lower

Percentage Accurate: 60.7% → 99.4%
Time: 11.8s
Alternatives: 15
Speedup: 11.4×

Specification

?
\[\left(0 \leq s \land s \leq 256\right) \land \left(2.328306437 \cdot 10^{-10} \leq u \land u \leq 0.25\right)\]
\[\begin{array}{l} \\ s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right) \end{array} \]
(FPCore (s u) :precision binary32 (* s (log (/ 1.0 (- 1.0 (* 4.0 u))))))
float code(float s, float u) {
	return s * logf((1.0f / (1.0f - (4.0f * u))));
}
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
function code(s, u)
	return Float32(s * log(Float32(Float32(1.0) / Float32(Float32(1.0) - Float32(Float32(4.0) * u)))))
end
function tmp = code(s, u)
	tmp = s * log((single(1.0) / (single(1.0) - (single(4.0) * u))));
end
\begin{array}{l}

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

Sampling outcomes in binary32 precision:

Local Percentage Accuracy vs ?

The average percentage accuracy by input value. Horizontal axis shows value of an input variable; the variable is choosen in the title. Vertical axis is accuracy; higher is better. Red represent the original program, while blue represents Herbie's suggestion. These can be toggled with buttons below the plot. The line is an average while dots represent individual samples.

Accuracy vs Speed?

Herbie found 15 alternatives:

AlternativeAccuracySpeedup
The accuracy (vertical axis) and speed (horizontal axis) of each alternatives. Up and to the right is better. The red square shows the initial program, and each blue circle shows an alternative.The line shows the best available speed-accuracy tradeoffs.

Initial Program: 60.7% accurate, 1.0× speedup?

\[\begin{array}{l} \\ s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right) \end{array} \]
(FPCore (s u) :precision binary32 (* s (log (/ 1.0 (- 1.0 (* 4.0 u))))))
float code(float s, float u) {
	return s * logf((1.0f / (1.0f - (4.0f * u))));
}
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
function code(s, u)
	return Float32(s * log(Float32(Float32(1.0) / Float32(Float32(1.0) - Float32(Float32(4.0) * u)))))
end
function tmp = code(s, u)
	tmp = s * log((single(1.0) / (single(1.0) - (single(4.0) * u))));
end
\begin{array}{l}

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

Alternative 1: 99.4% accurate, 1.1× speedup?

\[\begin{array}{l} \\ \mathsf{log1p}\left(u \cdot -4\right) \cdot \left(-s\right) \end{array} \]
(FPCore (s u) :precision binary32 (* (log1p (* u -4.0)) (- s)))
float code(float s, float u) {
	return log1pf((u * -4.0f)) * -s;
}
function code(s, u)
	return Float32(log1p(Float32(u * Float32(-4.0))) * Float32(-s))
end
\begin{array}{l}

\\
\mathsf{log1p}\left(u \cdot -4\right) \cdot \left(-s\right)
\end{array}
Derivation
  1. Initial program 59.1%

    \[s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right) \]
  2. Add Preprocessing
  3. Taylor expanded in s around 0

    \[\leadsto \color{blue}{s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right)} \]
  4. Step-by-step derivation
    1. *-commutativeN/A

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

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\log \left(1 - 4 \cdot u\right)\right)\right)} \cdot s \]
    3. distribute-lft-neg-outN/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(\log \left(1 - 4 \cdot u\right) \cdot s\right)} \]
    4. distribute-rgt-neg-inN/A

      \[\leadsto \color{blue}{\log \left(1 - 4 \cdot u\right) \cdot \left(\mathsf{neg}\left(s\right)\right)} \]
    5. lower-*.f32N/A

      \[\leadsto \color{blue}{\log \left(1 - 4 \cdot u\right) \cdot \left(\mathsf{neg}\left(s\right)\right)} \]
    6. cancel-sign-sub-invN/A

      \[\leadsto \log \color{blue}{\left(1 + \left(\mathsf{neg}\left(4\right)\right) \cdot u\right)} \cdot \left(\mathsf{neg}\left(s\right)\right) \]
    7. metadata-evalN/A

      \[\leadsto \log \left(1 + \color{blue}{-4} \cdot u\right) \cdot \left(\mathsf{neg}\left(s\right)\right) \]
    8. lower-log1p.f32N/A

      \[\leadsto \color{blue}{\mathsf{log1p}\left(-4 \cdot u\right)} \cdot \left(\mathsf{neg}\left(s\right)\right) \]
    9. *-commutativeN/A

      \[\leadsto \mathsf{log1p}\left(\color{blue}{u \cdot -4}\right) \cdot \left(\mathsf{neg}\left(s\right)\right) \]
    10. lower-*.f32N/A

      \[\leadsto \mathsf{log1p}\left(\color{blue}{u \cdot -4}\right) \cdot \left(\mathsf{neg}\left(s\right)\right) \]
    11. lower-neg.f3299.4

      \[\leadsto \mathsf{log1p}\left(u \cdot -4\right) \cdot \color{blue}{\left(-s\right)} \]
  5. Simplified99.4%

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

Alternative 2: 94.2% accurate, 1.5× speedup?

\[\begin{array}{l} \\ \left(u \cdot \mathsf{fma}\left(u, u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u \cdot s, 967.1111111111111, s \cdot 341.3333333333333\right), s \cdot 64\right), s \cdot -16\right)\right) \cdot \frac{1}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, 21.333333333333332\right), 8\right), -4\right)} \end{array} \]
(FPCore (s u)
 :precision binary32
 (*
  (*
   u
   (fma
    u
    (*
     u
     (fma
      u
      (fma (* u s) 967.1111111111111 (* s 341.3333333333333))
      (* s 64.0)))
    (* s -16.0)))
  (/ 1.0 (fma u (fma u (fma u 64.0 21.333333333333332) 8.0) -4.0))))
float code(float s, float u) {
	return (u * fmaf(u, (u * fmaf(u, fmaf((u * s), 967.1111111111111f, (s * 341.3333333333333f)), (s * 64.0f))), (s * -16.0f))) * (1.0f / fmaf(u, fmaf(u, fmaf(u, 64.0f, 21.333333333333332f), 8.0f), -4.0f));
}
function code(s, u)
	return Float32(Float32(u * fma(u, Float32(u * fma(u, fma(Float32(u * s), Float32(967.1111111111111), Float32(s * Float32(341.3333333333333))), Float32(s * Float32(64.0)))), Float32(s * Float32(-16.0)))) * Float32(Float32(1.0) / fma(u, fma(u, fma(u, Float32(64.0), Float32(21.333333333333332)), Float32(8.0)), Float32(-4.0))))
end
\begin{array}{l}

\\
\left(u \cdot \mathsf{fma}\left(u, u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u \cdot s, 967.1111111111111, s \cdot 341.3333333333333\right), s \cdot 64\right), s \cdot -16\right)\right) \cdot \frac{1}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, 21.333333333333332\right), 8\right), -4\right)}
\end{array}
Derivation
  1. Initial program 60.7%

    \[s \cdot \log \left(\frac{1}{1 - 4 \cdot u}\right) \]
  2. Add Preprocessing
  3. Taylor expanded in u around 0

    \[\leadsto s \cdot \color{blue}{\left(u \cdot \left(4 + u \cdot \left(8 + u \cdot \left(\frac{64}{3} + 64 \cdot u\right)\right)\right)\right)} \]
  4. Step-by-step derivation
    1. lower-*.f32N/A

      \[\leadsto s \cdot \color{blue}{\left(u \cdot \left(4 + u \cdot \left(8 + u \cdot \left(\frac{64}{3} + 64 \cdot u\right)\right)\right)\right)} \]
    2. +-commutativeN/A

      \[\leadsto s \cdot \left(u \cdot \color{blue}{\left(u \cdot \left(8 + u \cdot \left(\frac{64}{3} + 64 \cdot u\right)\right) + 4\right)}\right) \]
    3. lower-fma.f32N/A

      \[\leadsto s \cdot \left(u \cdot \color{blue}{\mathsf{fma}\left(u, 8 + u \cdot \left(\frac{64}{3} + 64 \cdot u\right), 4\right)}\right) \]
    4. +-commutativeN/A

      \[\leadsto s \cdot \left(u \cdot \mathsf{fma}\left(u, \color{blue}{u \cdot \left(\frac{64}{3} + 64 \cdot u\right) + 8}, 4\right)\right) \]
    5. lower-fma.f32N/A

      \[\leadsto s \cdot \left(u \cdot \mathsf{fma}\left(u, \color{blue}{\mathsf{fma}\left(u, \frac{64}{3} + 64 \cdot u, 8\right)}, 4\right)\right) \]
    6. +-commutativeN/A

      \[\leadsto s \cdot \left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, \color{blue}{64 \cdot u + \frac{64}{3}}, 8\right), 4\right)\right) \]
    7. *-commutativeN/A

      \[\leadsto s \cdot \left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, \color{blue}{u \cdot 64} + \frac{64}{3}, 8\right), 4\right)\right) \]
    8. lower-fma.f3293.3

      \[\leadsto s \cdot \left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, \color{blue}{\mathsf{fma}\left(u, 64, 21.333333333333332\right)}, 8\right), 4\right)\right) \]
  5. Simplified93.3%

    \[\leadsto s \cdot \color{blue}{\left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, 21.333333333333332\right), 8\right), 4\right)\right)} \]
  6. Step-by-step derivation
    1. lift-fma.f32N/A

      \[\leadsto s \cdot \left(u \cdot \left(u \cdot \left(u \cdot \color{blue}{\mathsf{fma}\left(u, 64, \frac{64}{3}\right)} + 8\right) + 4\right)\right) \]
    2. lift-fma.f32N/A

      \[\leadsto s \cdot \left(u \cdot \left(u \cdot \color{blue}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right)} + 4\right)\right) \]
    3. lift-fma.f32N/A

      \[\leadsto s \cdot \left(u \cdot \color{blue}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right), 4\right)}\right) \]
    4. associate-*r*N/A

      \[\leadsto \color{blue}{\left(s \cdot u\right) \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right), 4\right)} \]
    5. lift-fma.f32N/A

      \[\leadsto \left(s \cdot u\right) \cdot \color{blue}{\left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right) + 4\right)} \]
    6. flip-+N/A

      \[\leadsto \left(s \cdot u\right) \cdot \color{blue}{\frac{\left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right)\right) \cdot \left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right)\right) - 4 \cdot 4}{u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right) - 4}} \]
    7. associate-*r/N/A

      \[\leadsto \color{blue}{\frac{\left(s \cdot u\right) \cdot \left(\left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right)\right) \cdot \left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right)\right) - 4 \cdot 4\right)}{u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right) - 4}} \]
    8. div-invN/A

      \[\leadsto \color{blue}{\left(\left(s \cdot u\right) \cdot \left(\left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right)\right) \cdot \left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right)\right) - 4 \cdot 4\right)\right) \cdot \frac{1}{u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right) - 4}} \]
    9. lower-*.f32N/A

      \[\leadsto \color{blue}{\left(\left(s \cdot u\right) \cdot \left(\left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right)\right) \cdot \left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right)\right) - 4 \cdot 4\right)\right) \cdot \frac{1}{u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right) - 4}} \]
  7. Applied egg-rr92.9%

    \[\leadsto \color{blue}{\left(\left(u \cdot s\right) \cdot \mathsf{fma}\left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, 21.333333333333332\right), 8\right), u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, 21.333333333333332\right), 8\right), -16\right)\right) \cdot \frac{1}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, 21.333333333333332\right), 8\right), -4\right)}} \]
  8. Taylor expanded in u around 0

    \[\leadsto \left(\left(u \cdot s\right) \cdot \mathsf{fma}\left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right), u \cdot \mathsf{fma}\left(u, \color{blue}{\frac{64}{3}}, 8\right), -16\right)\right) \cdot \frac{1}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right), -4\right)} \]
  9. Step-by-step derivation
    1. Simplified93.7%

      \[\leadsto \left(\left(u \cdot s\right) \cdot \mathsf{fma}\left(u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, 21.333333333333332\right), 8\right), u \cdot \mathsf{fma}\left(u, \color{blue}{21.333333333333332}, 8\right), -16\right)\right) \cdot \frac{1}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, 21.333333333333332\right), 8\right), -4\right)} \]
    2. Taylor expanded in u around 0

      \[\leadsto \color{blue}{\left(u \cdot \left(-16 \cdot s + {u}^{2} \cdot \left(64 \cdot s + u \cdot \left(\frac{1024}{3} \cdot s + \frac{8704}{9} \cdot \left(s \cdot u\right)\right)\right)\right)\right)} \cdot \frac{1}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right), -4\right)} \]
    3. Step-by-step derivation
      1. lower-*.f32N/A

        \[\leadsto \color{blue}{\left(u \cdot \left(-16 \cdot s + {u}^{2} \cdot \left(64 \cdot s + u \cdot \left(\frac{1024}{3} \cdot s + \frac{8704}{9} \cdot \left(s \cdot u\right)\right)\right)\right)\right)} \cdot \frac{1}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right), -4\right)} \]
      2. +-commutativeN/A

        \[\leadsto \left(u \cdot \color{blue}{\left({u}^{2} \cdot \left(64 \cdot s + u \cdot \left(\frac{1024}{3} \cdot s + \frac{8704}{9} \cdot \left(s \cdot u\right)\right)\right) + -16 \cdot s\right)}\right) \cdot \frac{1}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right), -4\right)} \]
      3. unpow2N/A

        \[\leadsto \left(u \cdot \left(\color{blue}{\left(u \cdot u\right)} \cdot \left(64 \cdot s + u \cdot \left(\frac{1024}{3} \cdot s + \frac{8704}{9} \cdot \left(s \cdot u\right)\right)\right) + -16 \cdot s\right)\right) \cdot \frac{1}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right), -4\right)} \]
      4. associate-*l*N/A

        \[\leadsto \left(u \cdot \left(\color{blue}{u \cdot \left(u \cdot \left(64 \cdot s + u \cdot \left(\frac{1024}{3} \cdot s + \frac{8704}{9} \cdot \left(s \cdot u\right)\right)\right)\right)} + -16 \cdot s\right)\right) \cdot \frac{1}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right), -4\right)} \]
      5. lower-fma.f32N/A

        \[\leadsto \left(u \cdot \color{blue}{\mathsf{fma}\left(u, u \cdot \left(64 \cdot s + u \cdot \left(\frac{1024}{3} \cdot s + \frac{8704}{9} \cdot \left(s \cdot u\right)\right)\right), -16 \cdot s\right)}\right) \cdot \frac{1}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, \frac{64}{3}\right), 8\right), -4\right)} \]
    4. Simplified94.2%

      \[\leadsto \color{blue}{\left(u \cdot \mathsf{fma}\left(u, u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(s \cdot u, 967.1111111111111, s \cdot 341.3333333333333\right), s \cdot 64\right), s \cdot -16\right)\right)} \cdot \frac{1}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, 21.333333333333332\right), 8\right), -4\right)} \]
    5. Final simplification94.2%

      \[\leadsto \left(u \cdot \mathsf{fma}\left(u, u \cdot \mathsf{fma}\left(u, \mathsf{fma}\left(u \cdot s, 967.1111111111111, s \cdot 341.3333333333333\right), s \cdot 64\right), s \cdot -16\right)\right) \cdot \frac{1}{\mathsf{fma}\left(u, \mathsf{fma}\left(u, \mathsf{fma}\left(u, 64, 21.333333333333332\right), 8\right), -4\right)} \]
    6. Add Preprocessing

    Reproduce

    ?
    herbie shell --seed 2024218 
    (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))))))