Disney BSSRDF, sample scattering profile, lower

Percentage Accurate: 60.9% → 99.4%

Time: 8.6s

Alternatives: 12

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: 60.9% 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 60.7%

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

   1. log-rec 63.3%

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

   2. distribute-rgt-neg-out 63.3%

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

   3. distribute-lft-neg-out 63.3%

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

   4. cancel-sign-sub-inv 63.3%

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

   5. log1p-define 99.4%

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

   6. *-commutative 99.4%

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

   7. metadata-eval 99.4%

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

3. Simplified 99.4%

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

Alternative 2: 93.4% accurate, 5.2× speedup

Math

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

FPCore

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

C

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

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

Julia

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

MATLAB

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

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 93.9%

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

4. Taylor expanded in s around 0 93.9%

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

   1. *-commutative 93.9%

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

6. Simplified 93.9%

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

7. Final simplification 93.9%

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

Alternative 3: 93.4% accurate, 5.7× speedup

Math

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

FPCore

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

C

float code(float s, float u) {
	return u * ((s * 4.0f) + (s * (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 = u * ((s * 4.0e0) + (s * (u * (8.0e0 + (u * (21.333333333333332e0 + (u * 64.0e0)))))))
end function

Julia

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

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 93.9%

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

4. Taylor expanded in s around 0 93.9%

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

   1. *-commutative 93.9%

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

6. Simplified 93.9%

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

7. Final simplification 93.9%

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

Alternative 4: 93.1% accurate, 5.7× speedup

Math

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

FPCore

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

C

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

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

MATLAB

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

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 93.8%

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

   1. *-commutative 93.8%

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

5. Simplified 93.8%

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

6. Taylor expanded in u around inf 93.8%

   \[\leadsto s \cdot \left(u \cdot \left(4 + u \cdot \left(8 + u \cdot \color{blue}{\left(u \cdot \left(64 + 21.333333333333332 \cdot \frac{1}{u}\right)\right)}\right)\right)\right) \]
7. Step-by-step derivation

   1. associate-*r/ 93.8%

      \[\leadsto s \cdot \left(u \cdot \left(4 + u \cdot \left(8 + u \cdot \left(u \cdot \left(64 + \color{blue}{\frac{21.333333333333332 \cdot 1}{u}}\right)\right)\right)\right)\right) \]

   2. metadata-eval 93.8%

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

8. Simplified 93.8%

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

Alternative 5: 93.1% 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 60.7%

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

3. Taylor expanded in u around 0 93.8%

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

   1. *-commutative 93.8%

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

5. Simplified 93.8%

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

Alternative 6: 91.1% accurate, 7.3× speedup

Math

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

FPCore

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

C

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

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 + (8.0e0 / u))))))
end function

Julia

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

MATLAB

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

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 91.6%

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

   1. *-commutative 91.6%

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

5. Simplified 91.6%

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

6. Taylor expanded in u around inf 91.6%

   \[\leadsto s \cdot \left(u \cdot \left(4 + u \cdot \color{blue}{\left(u \cdot \left(21.333333333333332 + 8 \cdot \frac{1}{u}\right)\right)}\right)\right) \]
7. Step-by-step derivation

   1. associate-*r/ 91.6%

      \[\leadsto s \cdot \left(u \cdot \left(4 + u \cdot \left(u \cdot \left(21.333333333333332 + \color{blue}{\frac{8 \cdot 1}{u}}\right)\right)\right)\right) \]

   2. metadata-eval 91.6%

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

8. Simplified 91.6%

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

Alternative 7: 91.1% 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 60.7%

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

3. Taylor expanded in u around 0 91.6%

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

   1. *-commutative 91.6%

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

5. Simplified 91.6%

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

Alternative 8: 87.2% accurate, 9.9× speedup

Math

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

FPCore

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

C

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

Fortran

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

Julia

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

MATLAB

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

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 87.4%

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

4. Final simplification 87.4%

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

Alternative 9: 87.0% 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 60.7%

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

3. Taylor expanded in u around 0 87.2%

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

   1. *-commutative 87.2%

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

5. Simplified 87.2%

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

Alternative 10: 74.3% 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 60.7%

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

3. Taylor expanded in u around 0 73.6%

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

4. Final simplification 73.6%

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

Alternative 11: 74.1% 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 60.7%

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

3. Taylor expanded in u around 0 73.5%

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

   1. *-commutative 73.5%

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

5. Simplified 73.5%

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

6. Final simplification 73.5%

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

Alternative 12: 16.6% accurate, 109.0× speedup

Math

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

FPCore

(FPCore (s u) :precision binary32 0.0)

C

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

Fortran

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

Julia

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

MATLAB

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

Derivation

1. Initial program 60.7%

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

4. Applied egg-rr 15.5%

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

   1. +-inverses 15.5%

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

6. Simplified 15.5%

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

7. Taylor expanded in s around 0 15.5%

   \[\leadsto \color{blue}{0} \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2024119 
(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))))))