Disney BSSRDF, sample scattering profile, lower

Percentage Accurate: 61.2% → 99.4%

Time: 13.4s

Alternatives: 10

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: 61.2% 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 59.8%

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

   1. log-rec N/A

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

   2. neg-mul-1 N/A

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

   3. associate-*r* N/A

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

   4. *-commutative N/A

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

   5. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(\log \left(1 - 4 \cdot u\right), \color{blue}{\left(s \cdot -1\right)}\right) \]

   6. sub-neg N/A

      \[\leadsto \mathsf{*.f32}\left(\log \left(1 + \left(\mathsf{neg}\left(4 \cdot u\right)\right)\right), \left(s \cdot -1\right)\right) \]

   7. accelerator-lowering-log1p.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\left(\mathsf{neg}\left(4 \cdot u\right)\right)\right), \left(\color{blue}{s} \cdot -1\right)\right) \]

   8. *-commutative N/A

      \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\left(\mathsf{neg}\left(u \cdot 4\right)\right)\right), \left(s \cdot -1\right)\right) \]

   9. distribute-rgt-neg-in N/A

      \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\left(u \cdot \left(\mathsf{neg}\left(4\right)\right)\right)\right), \left(s \cdot -1\right)\right) \]

   10. *-lowering-*.f32 N/A

       \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{*.f32}\left(u, \left(\mathsf{neg}\left(4\right)\right)\right)\right), \left(s \cdot -1\right)\right) \]

   11. metadata-eval N/A

       \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{*.f32}\left(u, -4\right)\right), \left(s \cdot -1\right)\right) \]

   12. *-commutative N/A

       \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{*.f32}\left(u, -4\right)\right), \left(-1 \cdot \color{blue}{s}\right)\right) \]

   13. neg-mul-1 N/A

       \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{*.f32}\left(u, -4\right)\right), \left(\mathsf{neg}\left(s\right)\right)\right) \]

   14. neg-lowering-neg.f32 99.3%

       \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{*.f32}\left(u, -4\right)\right), \mathsf{neg.f32}\left(s\right)\right) \]

3. Simplified 99.3%

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

Alternative 2: 93.5% accurate, 5.2× speedup

Math

\[\begin{array}{l} \\ \left(u \cdot u\right) \cdot \left(s \cdot \left(8 + u \cdot \left(21.333333333333332 + u \cdot 64\right)\right)\right) + s \cdot \left(u \cdot 4\right) \end{array} \]

FPCore

(FPCore (s u)
 :precision binary32
 (+
  (* (* u u) (* s (+ 8.0 (* u (+ 21.333333333333332 (* u 64.0))))))
  (* s (* u 4.0))))

C

float code(float s, float u) {
	return ((u * u) * (s * (8.0f + (u * (21.333333333333332f + (u * 64.0f)))))) + (s * (u * 4.0f));
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = ((u * u) * (s * (8.0e0 + (u * (21.333333333333332e0 + (u * 64.0e0)))))) + (s * (u * 4.0e0))
end function

Julia

function code(s, u)
	return Float32(Float32(Float32(u * u) * Float32(s * Float32(Float32(8.0) + Float32(u * Float32(Float32(21.333333333333332) + Float32(u * Float32(64.0))))))) + Float32(s * Float32(u * Float32(4.0))))
end

MATLAB

function tmp = code(s, u)
	tmp = ((u * u) * (s * (single(8.0) + (u * (single(21.333333333333332) + (u * single(64.0))))))) + (s * (u * single(4.0)));
end

Derivation

1. Initial program 59.8%

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

3. Taylor expanded in u around 0

   \[\leadsto \color{blue}{u \cdot \left(4 \cdot s + u \cdot \left(8 \cdot s + u \cdot \left(\frac{64}{3} \cdot s + 64 \cdot \left(s \cdot u\right)\right)\right)\right)} \]

5. Simplified 93.0%

   \[\leadsto \color{blue}{u \cdot \left(4 \cdot s + \left(u \cdot s\right) \cdot \left(8 + u \cdot \left(21.333333333333332 + u \cdot 64\right)\right)\right)} \]
6. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto u \cdot \left(\left(u \cdot s\right) \cdot \left(8 + u \cdot \left(\frac{64}{3} + u \cdot 64\right)\right) + \color{blue}{4 \cdot s}\right) \]

   2. distribute-lft-in N/A

      \[\leadsto u \cdot \left(\left(u \cdot s\right) \cdot \left(8 + u \cdot \left(\frac{64}{3} + u \cdot 64\right)\right)\right) + \color{blue}{u \cdot \left(4 \cdot s\right)} \]

   3. *-commutative N/A

      \[\leadsto \left(\left(u \cdot s\right) \cdot \left(8 + u \cdot \left(\frac{64}{3} + u \cdot 64\right)\right)\right) \cdot u + \color{blue}{u} \cdot \left(4 \cdot s\right) \]

   4. *-commutative N/A

      \[\leadsto \left(\left(u \cdot s\right) \cdot \left(8 + u \cdot \left(\frac{64}{3} + u \cdot 64\right)\right)\right) \cdot u + u \cdot \left(s \cdot \color{blue}{4}\right) \]

   5. associate-*l* N/A

      \[\leadsto \left(\left(u \cdot s\right) \cdot \left(8 + u \cdot \left(\frac{64}{3} + u \cdot 64\right)\right)\right) \cdot u + \left(u \cdot s\right) \cdot \color{blue}{4} \]

   6. +-lowering-+.f32 N/A

      \[\leadsto \mathsf{+.f32}\left(\left(\left(\left(u \cdot s\right) \cdot \left(8 + u \cdot \left(\frac{64}{3} + u \cdot 64\right)\right)\right) \cdot u\right), \color{blue}{\left(\left(u \cdot s\right) \cdot 4\right)}\right) \]

7. Applied egg-rr 93.1%

   \[\leadsto \color{blue}{\left(u \cdot u\right) \cdot \left(s \cdot \left(8 + u \cdot \left(21.333333333333332 + u \cdot 64\right)\right)\right) + s \cdot \left(4 \cdot u\right)} \]

8. Final simplification 93.1%

   \[\leadsto \left(u \cdot u\right) \cdot \left(s \cdot \left(8 + u \cdot \left(21.333333333333332 + u \cdot 64\right)\right)\right) + s \cdot \left(u \cdot 4\right) \]
9. Add Preprocessing

Alternative 3: 93.6% accurate, 5.7× speedup

Math

\[\begin{array}{l} \\ s \cdot \left(u \cdot 4 + \left(u \cdot u\right) \cdot \left(8 + u \cdot \left(21.333333333333332 + u \cdot 64\right)\right)\right) \end{array} \]

FPCore

(FPCore (s u)
 :precision binary32
 (*
  s
  (+ (* u 4.0) (* (* u u) (+ 8.0 (* u (+ 21.333333333333332 (* u 64.0))))))))

C

float code(float s, float u) {
	return s * ((u * 4.0f) + ((u * u) * (8.0f + (u * (21.333333333333332f + (u * 64.0f))))));
}

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 + (u * (21.333333333333332e0 + (u * 64.0e0))))))
end function

Julia

function code(s, u)
	return Float32(s * Float32(Float32(u * Float32(4.0)) + Float32(Float32(u * u) * Float32(Float32(8.0) + Float32(u * Float32(Float32(21.333333333333332) + Float32(u * Float32(64.0))))))))
end

MATLAB

function tmp = code(s, u)
	tmp = s * ((u * single(4.0)) + ((u * u) * (single(8.0) + (u * (single(21.333333333333332) + (u * single(64.0)))))));
end

Derivation

1. Initial program 59.8%

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

   1. log-rec N/A

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

   2. neg-mul-1 N/A

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

   3. associate-*r* N/A

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

   4. *-commutative N/A

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

   5. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(\log \left(1 - 4 \cdot u\right), \color{blue}{\left(s \cdot -1\right)}\right) \]

   6. sub-neg N/A

      \[\leadsto \mathsf{*.f32}\left(\log \left(1 + \left(\mathsf{neg}\left(4 \cdot u\right)\right)\right), \left(s \cdot -1\right)\right) \]

   7. accelerator-lowering-log1p.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\left(\mathsf{neg}\left(4 \cdot u\right)\right)\right), \left(\color{blue}{s} \cdot -1\right)\right) \]

   8. *-commutative N/A

      \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\left(\mathsf{neg}\left(u \cdot 4\right)\right)\right), \left(s \cdot -1\right)\right) \]

   9. distribute-rgt-neg-in N/A

      \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\left(u \cdot \left(\mathsf{neg}\left(4\right)\right)\right)\right), \left(s \cdot -1\right)\right) \]

   10. *-lowering-*.f32 N/A

       \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{*.f32}\left(u, \left(\mathsf{neg}\left(4\right)\right)\right)\right), \left(s \cdot -1\right)\right) \]

   11. metadata-eval N/A

       \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{*.f32}\left(u, -4\right)\right), \left(s \cdot -1\right)\right) \]

   12. *-commutative N/A

       \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{*.f32}\left(u, -4\right)\right), \left(-1 \cdot \color{blue}{s}\right)\right) \]

   13. neg-mul-1 N/A

       \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{*.f32}\left(u, -4\right)\right), \left(\mathsf{neg}\left(s\right)\right)\right) \]

   14. neg-lowering-neg.f32 99.3%

       \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{*.f32}\left(u, -4\right)\right), \mathsf{neg.f32}\left(s\right)\right) \]

3. Simplified 99.3%

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

6. Applied egg-rr 99.2%

   \[\leadsto \mathsf{log1p}\left(\color{blue}{\frac{\left(u \cdot \left(u \cdot u\right)\right) \cdot -64}{0 + \left(u \cdot \left(u \cdot 16\right) + 0 \cdot \left(4 \cdot u\right)\right)}}\right) \cdot \left(-s\right) \]
7. Step-by-step derivation

   1. +-lft-identity N/A

      \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{/.f32}\left(\mathsf{*.f32}\left(\mathsf{*.f32}\left(u, \mathsf{*.f32}\left(u, u\right)\right), -64\right), \left(u \cdot \left(u \cdot 16\right) + 0 \cdot \left(4 \cdot u\right)\right)\right)\right), \mathsf{neg.f32}\left(s\right)\right) \]

   2. mul0-lft N/A

      \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{/.f32}\left(\mathsf{*.f32}\left(\mathsf{*.f32}\left(u, \mathsf{*.f32}\left(u, u\right)\right), -64\right), \left(u \cdot \left(u \cdot 16\right) + 0\right)\right)\right), \mathsf{neg.f32}\left(s\right)\right) \]

   3. +-commutative N/A

      \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{/.f32}\left(\mathsf{*.f32}\left(\mathsf{*.f32}\left(u, \mathsf{*.f32}\left(u, u\right)\right), -64\right), \left(0 + u \cdot \left(u \cdot 16\right)\right)\right)\right), \mathsf{neg.f32}\left(s\right)\right) \]

   4. +-lft-identity N/A

      \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{/.f32}\left(\mathsf{*.f32}\left(\mathsf{*.f32}\left(u, \mathsf{*.f32}\left(u, u\right)\right), -64\right), \left(u \cdot \left(u \cdot 16\right)\right)\right)\right), \mathsf{neg.f32}\left(s\right)\right) \]

   5. associate-*r* N/A

      \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{/.f32}\left(\mathsf{*.f32}\left(\mathsf{*.f32}\left(u, \mathsf{*.f32}\left(u, u\right)\right), -64\right), \left(\left(u \cdot u\right) \cdot 16\right)\right)\right), \mathsf{neg.f32}\left(s\right)\right) \]

   6. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{/.f32}\left(\mathsf{*.f32}\left(\mathsf{*.f32}\left(u, \mathsf{*.f32}\left(u, u\right)\right), -64\right), \mathsf{*.f32}\left(\left(u \cdot u\right), 16\right)\right)\right), \mathsf{neg.f32}\left(s\right)\right) \]

   7. *-lowering-*.f32 99.2%

      \[\leadsto \mathsf{*.f32}\left(\mathsf{log1p.f32}\left(\mathsf{/.f32}\left(\mathsf{*.f32}\left(\mathsf{*.f32}\left(u, \mathsf{*.f32}\left(u, u\right)\right), -64\right), \mathsf{*.f32}\left(\mathsf{*.f32}\left(u, u\right), 16\right)\right)\right), \mathsf{neg.f32}\left(s\right)\right) \]

8. Applied egg-rr 99.2%

   \[\leadsto \mathsf{log1p}\left(\frac{\left(u \cdot \left(u \cdot u\right)\right) \cdot -64}{\color{blue}{\left(u \cdot u\right) \cdot 16}}\right) \cdot \left(-s\right) \]

9. Taylor expanded in u around 0

   \[\leadsto \color{blue}{u \cdot \left(4 \cdot s + u \cdot \left(8 \cdot s + u \cdot \left(\frac{64}{3} \cdot s + 64 \cdot \left(s \cdot u\right)\right)\right)\right)} \]
10. Step-by-step derivation

    1. distribute-lft-in N/A

       \[\leadsto u \cdot \left(4 \cdot s\right) + \color{blue}{u \cdot \left(u \cdot \left(8 \cdot s + u \cdot \left(\frac{64}{3} \cdot s + 64 \cdot \left(s \cdot u\right)\right)\right)\right)} \]

    2. associate-*r* N/A

       \[\leadsto \left(u \cdot 4\right) \cdot s + \color{blue}{u} \cdot \left(u \cdot \left(8 \cdot s + u \cdot \left(\frac{64}{3} \cdot s + 64 \cdot \left(s \cdot u\right)\right)\right)\right) \]

    3. *-commutative N/A

       \[\leadsto \left(4 \cdot u\right) \cdot s + u \cdot \left(u \cdot \left(8 \cdot s + u \cdot \left(\frac{64}{3} \cdot s + 64 \cdot \left(s \cdot u\right)\right)\right)\right) \]

    4. associate-*r* N/A

       \[\leadsto \left(4 \cdot u\right) \cdot s + \left(u \cdot u\right) \cdot \color{blue}{\left(8 \cdot s + u \cdot \left(\frac{64}{3} \cdot s + 64 \cdot \left(s \cdot u\right)\right)\right)} \]

    5. unpow2 N/A

       \[\leadsto \left(4 \cdot u\right) \cdot s + {u}^{2} \cdot \left(\color{blue}{8 \cdot s} + u \cdot \left(\frac{64}{3} \cdot s + 64 \cdot \left(s \cdot u\right)\right)\right) \]

    6. distribute-rgt-in N/A

       \[\leadsto \left(4 \cdot u\right) \cdot s + {u}^{2} \cdot \left(8 \cdot s + \left(\left(\frac{64}{3} \cdot s\right) \cdot u + \color{blue}{\left(64 \cdot \left(s \cdot u\right)\right) \cdot u}\right)\right) \]

    7. associate-*r* N/A

       \[\leadsto \left(4 \cdot u\right) \cdot s + {u}^{2} \cdot \left(8 \cdot s + \left(\frac{64}{3} \cdot \left(s \cdot u\right) + \color{blue}{\left(64 \cdot \left(s \cdot u\right)\right)} \cdot u\right)\right) \]

    8. *-commutative N/A

       \[\leadsto \left(4 \cdot u\right) \cdot s + {u}^{2} \cdot \left(8 \cdot s + \left(\left(s \cdot u\right) \cdot \frac{64}{3} + \color{blue}{\left(64 \cdot \left(s \cdot u\right)\right)} \cdot u\right)\right) \]

    9. *-commutative N/A

       \[\leadsto \left(4 \cdot u\right) \cdot s + {u}^{2} \cdot \left(8 \cdot s + \left(\left(s \cdot u\right) \cdot \frac{64}{3} + \left(\left(s \cdot u\right) \cdot 64\right) \cdot u\right)\right) \]

    10. associate-*l* N/A

        \[\leadsto \left(4 \cdot u\right) \cdot s + {u}^{2} \cdot \left(8 \cdot s + \left(\left(s \cdot u\right) \cdot \frac{64}{3} + \left(s \cdot u\right) \cdot \color{blue}{\left(64 \cdot u\right)}\right)\right) \]

    11. distribute-lft-in N/A

        \[\leadsto \left(4 \cdot u\right) \cdot s + {u}^{2} \cdot \left(8 \cdot s + \left(s \cdot u\right) \cdot \color{blue}{\left(\frac{64}{3} + 64 \cdot u\right)}\right) \]

    12. associate-*r* N/A

        \[\leadsto \left(4 \cdot u\right) \cdot s + {u}^{2} \cdot \left(8 \cdot s + s \cdot \color{blue}{\left(u \cdot \left(\frac{64}{3} + 64 \cdot u\right)\right)}\right) \]

    13. *-commutative N/A

        \[\leadsto \left(4 \cdot u\right) \cdot s + {u}^{2} \cdot \left(8 \cdot s + \left(u \cdot \left(\frac{64}{3} + 64 \cdot u\right)\right) \cdot \color{blue}{s}\right) \]

    14. distribute-rgt-in N/A

        \[\leadsto \left(4 \cdot u\right) \cdot s + {u}^{2} \cdot \left(s \cdot \color{blue}{\left(8 + u \cdot \left(\frac{64}{3} + 64 \cdot u\right)\right)}\right) \]

    15. associate-*l* N/A

        \[\leadsto \left(4 \cdot u\right) \cdot s + \left({u}^{2} \cdot s\right) \cdot \color{blue}{\left(8 + u \cdot \left(\frac{64}{3} + 64 \cdot u\right)\right)} \]

11. Simplified 93.0%

    \[\leadsto \color{blue}{s \cdot \left(u \cdot 4 + \left(u \cdot u\right) \cdot \left(8 + u \cdot \left(21.333333333333332 + u \cdot 64\right)\right)\right)} \]
12. Add Preprocessing

Alternative 4: 93.3% accurate, 6.4× speedup

Math

\[\begin{array}{l} \\ s \cdot \left(u \cdot \left(4 + u \cdot \left(8 + u \cdot \left(21.333333333333332 + u \cdot 64\right)\right)\right)\right) \end{array} \]

FPCore

(FPCore (s u)
 :precision binary32
 (* s (* u (+ 4.0 (* u (+ 8.0 (* u (+ 21.333333333333332 (* u 64.0)))))))))

C

float code(float s, float u) {
	return s * (u * (4.0f + (u * (8.0f + (u * (21.333333333333332f + (u * 64.0f)))))));
}

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 + (u * (21.333333333333332e0 + (u * 64.0e0)))))))
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(Float32(21.333333333333332) + Float32(u * Float32(64.0)))))))))
end

MATLAB

function tmp = code(s, u)
	tmp = s * (u * (single(4.0) + (u * (single(8.0) + (u * (single(21.333333333333332) + (u * single(64.0))))))));
end

Derivation

1. Initial program 59.8%

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

3. Taylor expanded in u around 0

   \[\leadsto \mathsf{*.f32}\left(s, \color{blue}{\left(u \cdot \left(4 + u \cdot \left(8 + u \cdot \left(\frac{64}{3} + 64 \cdot u\right)\right)\right)\right)}\right) \]
4. Step-by-step derivation

   1. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \color{blue}{\left(4 + u \cdot \left(8 + u \cdot \left(\frac{64}{3} + 64 \cdot u\right)\right)\right)}\right)\right) \]

   2. +-lowering-+.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \color{blue}{\left(u \cdot \left(8 + u \cdot \left(\frac{64}{3} + 64 \cdot u\right)\right)\right)}\right)\right)\right) \]

   3. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \mathsf{*.f32}\left(u, \color{blue}{\left(8 + u \cdot \left(\frac{64}{3} + 64 \cdot u\right)\right)}\right)\right)\right)\right) \]

   4. +-lowering-+.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(8, \color{blue}{\left(u \cdot \left(\frac{64}{3} + 64 \cdot u\right)\right)}\right)\right)\right)\right)\right) \]

   5. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(8, \mathsf{*.f32}\left(u, \color{blue}{\left(\frac{64}{3} + 64 \cdot u\right)}\right)\right)\right)\right)\right)\right) \]

   6. +-lowering-+.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(8, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(\frac{64}{3}, \color{blue}{\left(64 \cdot u\right)}\right)\right)\right)\right)\right)\right)\right) \]

   7. *-commutative N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(8, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(\frac{64}{3}, \left(u \cdot \color{blue}{64}\right)\right)\right)\right)\right)\right)\right)\right) \]

   8. *-lowering-*.f32 92.8%

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(8, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(\frac{64}{3}, \mathsf{*.f32}\left(u, \color{blue}{64}\right)\right)\right)\right)\right)\right)\right)\right) \]

5. Simplified 92.8%

   \[\leadsto s \cdot \color{blue}{\left(u \cdot \left(4 + u \cdot \left(8 + u \cdot \left(21.333333333333332 + u \cdot 64\right)\right)\right)\right)} \]
6. Add Preprocessing

Alternative 5: 91.4% accurate, 7.3× speedup

Math

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

FPCore

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

C

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

Fortran

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

Julia

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

MATLAB

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

Derivation

1. Initial program 59.8%

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

3. Taylor expanded in u around 0

   \[\leadsto \color{blue}{u \cdot \left(4 \cdot s + u \cdot \left(8 \cdot s + u \cdot \left(\frac{64}{3} \cdot s + 64 \cdot \left(s \cdot u\right)\right)\right)\right)} \]

5. Simplified 93.0%

   \[\leadsto \color{blue}{u \cdot \left(4 \cdot s + \left(u \cdot s\right) \cdot \left(8 + u \cdot \left(21.333333333333332 + u \cdot 64\right)\right)\right)} \]

6. Taylor expanded in u around 0

   \[\leadsto \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(\mathsf{*.f32}\left(4, s\right), \mathsf{*.f32}\left(\mathsf{*.f32}\left(u, s\right), \mathsf{+.f32}\left(8, \mathsf{*.f32}\left(u, \color{blue}{\frac{64}{3}}\right)\right)\right)\right)\right) \]
7. Step-by-step derivation

8. Simplified 90.9%

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

9. Final simplification 90.9%

   \[\leadsto u \cdot \left(s \cdot 4 + \left(u \cdot s\right) \cdot \left(8 + u \cdot 21.333333333333332\right)\right) \]
10. Add Preprocessing

Alternative 6: 91.2% 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 59.8%

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

3. Taylor expanded in u around 0

   \[\leadsto \mathsf{*.f32}\left(s, \color{blue}{\left(u \cdot \left(4 + u \cdot \left(8 + \frac{64}{3} \cdot u\right)\right)\right)}\right) \]
4. Step-by-step derivation

   1. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \color{blue}{\left(4 + u \cdot \left(8 + \frac{64}{3} \cdot u\right)\right)}\right)\right) \]

   2. +-lowering-+.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \color{blue}{\left(u \cdot \left(8 + \frac{64}{3} \cdot u\right)\right)}\right)\right)\right) \]

   3. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \mathsf{*.f32}\left(u, \color{blue}{\left(8 + \frac{64}{3} \cdot u\right)}\right)\right)\right)\right) \]

   4. +-lowering-+.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(8, \color{blue}{\left(\frac{64}{3} \cdot u\right)}\right)\right)\right)\right)\right) \]

   5. *-commutative N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(8, \left(u \cdot \color{blue}{\frac{64}{3}}\right)\right)\right)\right)\right)\right) \]

   6. *-lowering-*.f32 90.7%

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(8, \mathsf{*.f32}\left(u, \color{blue}{\frac{64}{3}}\right)\right)\right)\right)\right)\right) \]

5. Simplified 90.7%

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

Alternative 7: 87.0% accurate, 9.9× speedup

Math

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

FPCore

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

C

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

Fortran

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

Julia

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

MATLAB

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

Derivation

1. Initial program 59.8%

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

3. Taylor expanded in u around 0

   \[\leadsto \color{blue}{u \cdot \left(4 \cdot s + u \cdot \left(8 \cdot s + u \cdot \left(\frac{64}{3} \cdot s + 64 \cdot \left(s \cdot u\right)\right)\right)\right)} \]

5. Simplified 93.0%

   \[\leadsto \color{blue}{u \cdot \left(4 \cdot s + \left(u \cdot s\right) \cdot \left(8 + u \cdot \left(21.333333333333332 + u \cdot 64\right)\right)\right)} \]

6. Taylor expanded in u around 0

   \[\leadsto \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(\mathsf{*.f32}\left(4, s\right), \color{blue}{\left(8 \cdot \left(s \cdot u\right)\right)}\right)\right) \]
7. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(\mathsf{*.f32}\left(4, s\right), \left(\left(s \cdot u\right) \cdot \color{blue}{8}\right)\right)\right) \]

   2. associate-*l* N/A

      \[\leadsto \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(\mathsf{*.f32}\left(4, s\right), \left(s \cdot \color{blue}{\left(u \cdot 8\right)}\right)\right)\right) \]

   3. *-commutative N/A

      \[\leadsto \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(\mathsf{*.f32}\left(4, s\right), \left(s \cdot \left(8 \cdot \color{blue}{u}\right)\right)\right)\right) \]

   4. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(\mathsf{*.f32}\left(4, s\right), \mathsf{*.f32}\left(s, \color{blue}{\left(8 \cdot u\right)}\right)\right)\right) \]

   5. *-commutative N/A

      \[\leadsto \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(\mathsf{*.f32}\left(4, s\right), \mathsf{*.f32}\left(s, \left(u \cdot \color{blue}{8}\right)\right)\right)\right) \]

   6. *-lowering-*.f32 86.8%

      \[\leadsto \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(\mathsf{*.f32}\left(4, s\right), \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \color{blue}{8}\right)\right)\right)\right) \]

8. Simplified 86.8%

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

9. Final simplification 86.8%

   \[\leadsto u \cdot \left(s \cdot 4 + s \cdot \left(u \cdot 8\right)\right) \]
10. Add Preprocessing

Alternative 8: 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 59.8%

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

3. Taylor expanded in u around 0

   \[\leadsto \mathsf{*.f32}\left(s, \color{blue}{\left(u \cdot \left(4 + 8 \cdot u\right)\right)}\right) \]
4. Step-by-step derivation

   1. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \color{blue}{\left(4 + 8 \cdot u\right)}\right)\right) \]

   2. +-lowering-+.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \color{blue}{\left(8 \cdot u\right)}\right)\right)\right) \]

   3. *-commutative N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \left(u \cdot \color{blue}{8}\right)\right)\right)\right) \]

   4. *-lowering-*.f32 86.7%

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \mathsf{*.f32}\left(u, \color{blue}{8}\right)\right)\right)\right) \]

5. Simplified 86.7%

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

   1. +-commutative N/A

      \[\leadsto \mathsf{*.f32}\left(s, \left(u \cdot \left(u \cdot 8 + \color{blue}{4}\right)\right)\right) \]

   2. distribute-rgt-in N/A

      \[\leadsto \mathsf{*.f32}\left(s, \left(\left(u \cdot 8\right) \cdot u + \color{blue}{4 \cdot u}\right)\right) \]

   3. +-lowering-+.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{+.f32}\left(\left(\left(u \cdot 8\right) \cdot u\right), \color{blue}{\left(4 \cdot u\right)}\right)\right) \]

   4. *-commutative N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{+.f32}\left(\left(u \cdot \left(u \cdot 8\right)\right), \left(\color{blue}{4} \cdot u\right)\right)\right) \]

   5. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{+.f32}\left(\mathsf{*.f32}\left(u, \left(u \cdot 8\right)\right), \left(\color{blue}{4} \cdot u\right)\right)\right) \]

   6. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{+.f32}\left(\mathsf{*.f32}\left(u, \mathsf{*.f32}\left(u, 8\right)\right), \left(4 \cdot u\right)\right)\right) \]

   7. *-commutative N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{+.f32}\left(\mathsf{*.f32}\left(u, \mathsf{*.f32}\left(u, 8\right)\right), \left(u \cdot \color{blue}{4}\right)\right)\right) \]

   8. *-lowering-*.f32 86.8%

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{+.f32}\left(\mathsf{*.f32}\left(u, \mathsf{*.f32}\left(u, 8\right)\right), \mathsf{*.f32}\left(u, \color{blue}{4}\right)\right)\right) \]

7. Applied egg-rr 86.8%

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

8. Final simplification 86.8%

   \[\leadsto s \cdot \left(u \cdot 4 + u \cdot \left(u \cdot 8\right)\right) \]
9. Add Preprocessing

Alternative 9: 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 59.8%

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

3. Taylor expanded in u around 0

   \[\leadsto \mathsf{*.f32}\left(s, \color{blue}{\left(u \cdot \left(4 + 8 \cdot u\right)\right)}\right) \]
4. Step-by-step derivation

   1. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \color{blue}{\left(4 + 8 \cdot u\right)}\right)\right) \]

   2. +-lowering-+.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \color{blue}{\left(8 \cdot u\right)}\right)\right)\right) \]

   3. *-commutative N/A

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \left(u \cdot \color{blue}{8}\right)\right)\right)\right) \]

   4. *-lowering-*.f32 86.7%

      \[\leadsto \mathsf{*.f32}\left(s, \mathsf{*.f32}\left(u, \mathsf{+.f32}\left(4, \mathsf{*.f32}\left(u, \color{blue}{8}\right)\right)\right)\right) \]

5. Simplified 86.7%

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

Alternative 10: 73.9% 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 59.8%

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

3. Taylor expanded in u around 0

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

   1. *-commutative N/A

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

   2. associate-*r* N/A

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

   3. *-lowering-*.f32 N/A

      \[\leadsto \mathsf{*.f32}\left(\left(4 \cdot u\right), \color{blue}{s}\right) \]

   4. *-lowering-*.f32 74.2%

      \[\leadsto \mathsf{*.f32}\left(\mathsf{*.f32}\left(4, u\right), s\right) \]

5. Simplified 74.2%

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

6. Final simplification 74.2%

   \[\leadsto s \cdot \left(u \cdot 4\right) \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024186 
(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))))))