Disney BSSRDF, PDF of scattering profile

Percentage Accurate: 99.6% → 99.6%
Time: 15.1s
Alternatives: 19
Speedup: 1.0×

Specification

?
\[\left(0 \leq s \land s \leq 256\right) \land \left(10^{-6} < r \land r < 1000000\right)\]
\[\begin{array}{l} \\ \frac{0.25 \cdot e^{\frac{-r}{s}}}{\left(\left(2 \cdot \pi\right) \cdot s\right) \cdot r} + \frac{0.75 \cdot e^{\frac{-r}{3 \cdot s}}}{\left(\left(6 \cdot \pi\right) \cdot s\right) \cdot r} \end{array} \]
(FPCore (s r)
 :precision binary32
 (+
  (/ (* 0.25 (exp (/ (- r) s))) (* (* (* 2.0 PI) s) r))
  (/ (* 0.75 (exp (/ (- r) (* 3.0 s)))) (* (* (* 6.0 PI) s) r))))
float code(float s, float r) {
	return ((0.25f * expf((-r / s))) / (((2.0f * ((float) M_PI)) * s) * r)) + ((0.75f * expf((-r / (3.0f * s)))) / (((6.0f * ((float) M_PI)) * s) * r));
}
function code(s, r)
	return Float32(Float32(Float32(Float32(0.25) * exp(Float32(Float32(-r) / s))) / Float32(Float32(Float32(Float32(2.0) * Float32(pi)) * s) * r)) + Float32(Float32(Float32(0.75) * exp(Float32(Float32(-r) / Float32(Float32(3.0) * s)))) / Float32(Float32(Float32(Float32(6.0) * Float32(pi)) * s) * r)))
end
function tmp = code(s, r)
	tmp = ((single(0.25) * exp((-r / s))) / (((single(2.0) * single(pi)) * s) * r)) + ((single(0.75) * exp((-r / (single(3.0) * s)))) / (((single(6.0) * single(pi)) * s) * r));
end
\begin{array}{l}

\\
\frac{0.25 \cdot e^{\frac{-r}{s}}}{\left(\left(2 \cdot \pi\right) \cdot s\right) \cdot r} + \frac{0.75 \cdot e^{\frac{-r}{3 \cdot s}}}{\left(\left(6 \cdot \pi\right) \cdot s\right) \cdot r}
\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 19 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: 99.6% accurate, 1.0× speedup?

\[\begin{array}{l} \\ \frac{0.25 \cdot e^{\frac{-r}{s}}}{\left(\left(2 \cdot \pi\right) \cdot s\right) \cdot r} + \frac{0.75 \cdot e^{\frac{-r}{3 \cdot s}}}{\left(\left(6 \cdot \pi\right) \cdot s\right) \cdot r} \end{array} \]
(FPCore (s r)
 :precision binary32
 (+
  (/ (* 0.25 (exp (/ (- r) s))) (* (* (* 2.0 PI) s) r))
  (/ (* 0.75 (exp (/ (- r) (* 3.0 s)))) (* (* (* 6.0 PI) s) r))))
float code(float s, float r) {
	return ((0.25f * expf((-r / s))) / (((2.0f * ((float) M_PI)) * s) * r)) + ((0.75f * expf((-r / (3.0f * s)))) / (((6.0f * ((float) M_PI)) * s) * r));
}
function code(s, r)
	return Float32(Float32(Float32(Float32(0.25) * exp(Float32(Float32(-r) / s))) / Float32(Float32(Float32(Float32(2.0) * Float32(pi)) * s) * r)) + Float32(Float32(Float32(0.75) * exp(Float32(Float32(-r) / Float32(Float32(3.0) * s)))) / Float32(Float32(Float32(Float32(6.0) * Float32(pi)) * s) * r)))
end
function tmp = code(s, r)
	tmp = ((single(0.25) * exp((-r / s))) / (((single(2.0) * single(pi)) * s) * r)) + ((single(0.75) * exp((-r / (single(3.0) * s)))) / (((single(6.0) * single(pi)) * s) * r));
end
\begin{array}{l}

\\
\frac{0.25 \cdot e^{\frac{-r}{s}}}{\left(\left(2 \cdot \pi\right) \cdot s\right) \cdot r} + \frac{0.75 \cdot e^{\frac{-r}{3 \cdot s}}}{\left(\left(6 \cdot \pi\right) \cdot s\right) \cdot r}
\end{array}

Alternative 1: 99.6% accurate, 1.0× speedup?

\[\begin{array}{l} \\ \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\pi \cdot 6\right)\right)}, 0.75, \frac{0.125}{\left(r \cdot \left(s \cdot \pi\right)\right) \cdot e^{\frac{r}{s}}}\right) \end{array} \]
(FPCore (s r)
 :precision binary32
 (fma
  (/ (exp (/ r (* s -3.0))) (* r (* s (* PI 6.0))))
  0.75
  (/ 0.125 (* (* r (* s PI)) (exp (/ r s))))))
float code(float s, float r) {
	return fmaf((expf((r / (s * -3.0f))) / (r * (s * (((float) M_PI) * 6.0f)))), 0.75f, (0.125f / ((r * (s * ((float) M_PI))) * expf((r / s)))));
}
function code(s, r)
	return fma(Float32(exp(Float32(r / Float32(s * Float32(-3.0)))) / Float32(r * Float32(s * Float32(Float32(pi) * Float32(6.0))))), Float32(0.75), Float32(Float32(0.125) / Float32(Float32(r * Float32(s * Float32(pi))) * exp(Float32(r / s)))))
end
\begin{array}{l}

\\
\mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\pi \cdot 6\right)\right)}, 0.75, \frac{0.125}{\left(r \cdot \left(s \cdot \pi\right)\right) \cdot e^{\frac{r}{s}}}\right)
\end{array}
Derivation
  1. Initial program 99.6%

    \[\frac{0.25 \cdot e^{\frac{-r}{s}}}{\left(\left(2 \cdot \pi\right) \cdot s\right) \cdot r} + \frac{0.75 \cdot e^{\frac{-r}{3 \cdot s}}}{\left(\left(6 \cdot \pi\right) \cdot s\right) \cdot r} \]
  2. Add Preprocessing
  3. Applied rewrites98.8%

    \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\pi \cdot 6\right)\right)}, 0.75, e^{\frac{r}{-s}} \cdot \frac{0.125}{\pi \cdot \left(r \cdot s\right)}\right)} \]
  4. Step-by-step derivation
    1. lift-*.f32N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \color{blue}{e^{\frac{r}{\mathsf{neg}\left(s\right)}} \cdot \frac{\frac{1}{8}}{\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)}}\right) \]
    2. *-commutativeN/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \color{blue}{\frac{\frac{1}{8}}{\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)} \cdot e^{\frac{r}{\mathsf{neg}\left(s\right)}}}\right) \]
    3. lift-/.f32N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \color{blue}{\frac{\frac{1}{8}}{\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)}} \cdot e^{\frac{r}{\mathsf{neg}\left(s\right)}}\right) \]
    4. lift-exp.f32N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\frac{1}{8}}{\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)} \cdot \color{blue}{e^{\frac{r}{\mathsf{neg}\left(s\right)}}}\right) \]
    5. lift-/.f32N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\frac{1}{8}}{\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)} \cdot e^{\color{blue}{\frac{r}{\mathsf{neg}\left(s\right)}}}\right) \]
    6. lift-neg.f32N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\frac{1}{8}}{\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)} \cdot e^{\frac{r}{\color{blue}{\mathsf{neg}\left(s\right)}}}\right) \]
    7. distribute-frac-neg2N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\frac{1}{8}}{\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)} \cdot e^{\color{blue}{\mathsf{neg}\left(\frac{r}{s}\right)}}\right) \]
    8. lift-/.f32N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\frac{1}{8}}{\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)} \cdot e^{\mathsf{neg}\left(\color{blue}{\frac{r}{s}}\right)}\right) \]
    9. exp-negN/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\frac{1}{8}}{\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)} \cdot \color{blue}{\frac{1}{e^{\frac{r}{s}}}}\right) \]
    10. frac-timesN/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \color{blue}{\frac{\frac{1}{8} \cdot 1}{\left(\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)\right) \cdot e^{\frac{r}{s}}}}\right) \]
    11. metadata-evalN/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\color{blue}{\frac{1}{8}}}{\left(\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)\right) \cdot e^{\frac{r}{s}}}\right) \]
    12. lower-/.f32N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \color{blue}{\frac{\frac{1}{8}}{\left(\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)\right) \cdot e^{\frac{r}{s}}}}\right) \]
    13. lower-*.f32N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\frac{1}{8}}{\color{blue}{\left(\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)\right) \cdot e^{\frac{r}{s}}}}\right) \]
    14. lift-*.f32N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\frac{1}{8}}{\color{blue}{\left(\mathsf{PI}\left(\right) \cdot \left(r \cdot s\right)\right)} \cdot e^{\frac{r}{s}}}\right) \]
    15. *-commutativeN/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\frac{1}{8}}{\color{blue}{\left(\left(r \cdot s\right) \cdot \mathsf{PI}\left(\right)\right)} \cdot e^{\frac{r}{s}}}\right) \]
    16. lift-*.f32N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\frac{1}{8}}{\left(\color{blue}{\left(r \cdot s\right)} \cdot \mathsf{PI}\left(\right)\right) \cdot e^{\frac{r}{s}}}\right) \]
    17. associate-*r*N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\frac{1}{8}}{\color{blue}{\left(r \cdot \left(s \cdot \mathsf{PI}\left(\right)\right)\right)} \cdot e^{\frac{r}{s}}}\right) \]
    18. lift-*.f32N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\frac{1}{8}}{\left(r \cdot \color{blue}{\left(s \cdot \mathsf{PI}\left(\right)\right)}\right) \cdot e^{\frac{r}{s}}}\right) \]
    19. lift-*.f32N/A

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\mathsf{PI}\left(\right) \cdot 6\right)\right)}, \frac{3}{4}, \frac{\frac{1}{8}}{\color{blue}{\left(r \cdot \left(s \cdot \mathsf{PI}\left(\right)\right)\right)} \cdot e^{\frac{r}{s}}}\right) \]
    20. lower-exp.f3299.7

      \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\pi \cdot 6\right)\right)}, 0.75, \frac{0.125}{\left(r \cdot \left(s \cdot \pi\right)\right) \cdot \color{blue}{e^{\frac{r}{s}}}}\right) \]
  5. Applied rewrites99.7%

    \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{r}{s \cdot -3}}}{r \cdot \left(s \cdot \left(\pi \cdot 6\right)\right)}, 0.75, \color{blue}{\frac{0.125}{\left(r \cdot \left(s \cdot \pi\right)\right) \cdot e^{\frac{r}{s}}}}\right) \]
  6. Add Preprocessing

Alternative 2: 99.5% accurate, 1.1× speedup?

\[\begin{array}{l} \\ \frac{0.125 \cdot \left(\frac{e^{\frac{-r}{s}}}{s \cdot \pi} + \frac{{e}^{\left(\frac{r}{s} \cdot -0.3333333333333333\right)}}{s \cdot \pi}\right)}{r} \end{array} \]
(FPCore (s r)
 :precision binary32
 (/
  (*
   0.125
   (+
    (/ (exp (/ (- r) s)) (* s PI))
    (/ (pow E (* (/ r s) -0.3333333333333333)) (* s PI))))
  r))
float code(float s, float r) {
	return (0.125f * ((expf((-r / s)) / (s * ((float) M_PI))) + (powf(((float) M_E), ((r / s) * -0.3333333333333333f)) / (s * ((float) M_PI))))) / r;
}
function code(s, r)
	return Float32(Float32(Float32(0.125) * Float32(Float32(exp(Float32(Float32(-r) / s)) / Float32(s * Float32(pi))) + Float32((Float32(exp(1)) ^ Float32(Float32(r / s) * Float32(-0.3333333333333333))) / Float32(s * Float32(pi))))) / r)
end
function tmp = code(s, r)
	tmp = (single(0.125) * ((exp((-r / s)) / (s * single(pi))) + ((single(2.71828182845904523536) ^ ((r / s) * single(-0.3333333333333333))) / (s * single(pi))))) / r;
end
\begin{array}{l}

\\
\frac{0.125 \cdot \left(\frac{e^{\frac{-r}{s}}}{s \cdot \pi} + \frac{{e}^{\left(\frac{r}{s} \cdot -0.3333333333333333\right)}}{s \cdot \pi}\right)}{r}
\end{array}
Derivation
  1. Initial program 99.6%

    \[\frac{0.25 \cdot e^{\frac{-r}{s}}}{\left(\left(2 \cdot \pi\right) \cdot s\right) \cdot r} + \frac{0.75 \cdot e^{\frac{-r}{3 \cdot s}}}{\left(\left(6 \cdot \pi\right) \cdot s\right) \cdot r} \]
  2. Add Preprocessing
  3. Taylor expanded in r around inf

    \[\leadsto \color{blue}{\frac{\frac{1}{8} \cdot \frac{e^{-1 \cdot \frac{r}{s}}}{s \cdot \mathsf{PI}\left(\right)} + \frac{1}{8} \cdot \frac{e^{\frac{-1}{3} \cdot \frac{r}{s}}}{s \cdot \mathsf{PI}\left(\right)}}{r}} \]
  4. Step-by-step derivation
    1. distribute-lft-outN/A

      \[\leadsto \frac{\color{blue}{\frac{1}{8} \cdot \left(\frac{e^{-1 \cdot \frac{r}{s}}}{s \cdot \mathsf{PI}\left(\right)} + \frac{e^{\frac{-1}{3} \cdot \frac{r}{s}}}{s \cdot \mathsf{PI}\left(\right)}\right)}}{r} \]
    2. metadata-evalN/A

      \[\leadsto \frac{\color{blue}{\left(-1 \cdot \frac{-1}{8}\right)} \cdot \left(\frac{e^{-1 \cdot \frac{r}{s}}}{s \cdot \mathsf{PI}\left(\right)} + \frac{e^{\frac{-1}{3} \cdot \frac{r}{s}}}{s \cdot \mathsf{PI}\left(\right)}\right)}{r} \]
    3. associate-*r*N/A

      \[\leadsto \frac{\color{blue}{-1 \cdot \left(\frac{-1}{8} \cdot \left(\frac{e^{-1 \cdot \frac{r}{s}}}{s \cdot \mathsf{PI}\left(\right)} + \frac{e^{\frac{-1}{3} \cdot \frac{r}{s}}}{s \cdot \mathsf{PI}\left(\right)}\right)\right)}}{r} \]
    4. distribute-lft-outN/A

      \[\leadsto \frac{-1 \cdot \color{blue}{\left(\frac{-1}{8} \cdot \frac{e^{-1 \cdot \frac{r}{s}}}{s \cdot \mathsf{PI}\left(\right)} + \frac{-1}{8} \cdot \frac{e^{\frac{-1}{3} \cdot \frac{r}{s}}}{s \cdot \mathsf{PI}\left(\right)}\right)}}{r} \]
    5. lower-/.f32N/A

      \[\leadsto \color{blue}{\frac{-1 \cdot \left(\frac{-1}{8} \cdot \frac{e^{-1 \cdot \frac{r}{s}}}{s \cdot \mathsf{PI}\left(\right)} + \frac{-1}{8} \cdot \frac{e^{\frac{-1}{3} \cdot \frac{r}{s}}}{s \cdot \mathsf{PI}\left(\right)}\right)}{r}} \]
  5. Applied rewrites99.5%

    \[\leadsto \color{blue}{\frac{0.125 \cdot \left(\frac{e^{-\frac{r}{s}}}{s \cdot \pi} + \frac{e^{\frac{r}{s} \cdot -0.3333333333333333}}{s \cdot \pi}\right)}{r}} \]
  6. Step-by-step derivation
    1. Applied rewrites99.5%

      \[\leadsto \frac{0.125 \cdot \left(\frac{e^{-\frac{r}{s}}}{s \cdot \pi} + \frac{{e}^{\left(\frac{r}{s} \cdot -0.3333333333333333\right)}}{s \cdot \pi}\right)}{r} \]
    2. Final simplification99.5%

      \[\leadsto \frac{0.125 \cdot \left(\frac{e^{\frac{-r}{s}}}{s \cdot \pi} + \frac{{e}^{\left(\frac{r}{s} \cdot -0.3333333333333333\right)}}{s \cdot \pi}\right)}{r} \]
    3. Add Preprocessing

    Reproduce

    ?
    herbie shell --seed 2024230 
    (FPCore (s r)
      :name "Disney BSSRDF, PDF of scattering profile"
      :precision binary32
      :pre (and (and (<= 0.0 s) (<= s 256.0)) (and (< 1e-6 r) (< r 1000000.0)))
      (+ (/ (* 0.25 (exp (/ (- r) s))) (* (* (* 2.0 PI) s) r)) (/ (* 0.75 (exp (/ (- r) (* 3.0 s)))) (* (* (* 6.0 PI) s) r))))