Disney BSSRDF, sample scattering profile, upper

Percentage Accurate: 95.9% → 98.3%

Time: 14.4s

Alternatives: 7

Speedup: 1.0×

Specification

\[\left(0 \leq s \land s \leq 256\right) \land \left(0.25 \leq u \land u \leq 1\right)\]

Math

\[\begin{array}{l} \\ \left(3 \cdot s\right) \cdot \log \left(\frac{1}{1 - \frac{u - 0.25}{0.75}}\right) \end{array} \]

FPCore

(FPCore (s u)
 :precision binary32
 (* (* 3.0 s) (log (/ 1.0 (- 1.0 (/ (- u 0.25) 0.75))))))

C

float code(float s, float u) {
	return (3.0f * s) * logf((1.0f / (1.0f - ((u - 0.25f) / 0.75f))));
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = (3.0e0 * s) * log((1.0e0 / (1.0e0 - ((u - 0.25e0) / 0.75e0))))
end function

Julia

function code(s, u)
	return Float32(Float32(Float32(3.0) * s) * log(Float32(Float32(1.0) / Float32(Float32(1.0) - Float32(Float32(u - Float32(0.25)) / Float32(0.75))))))
end

MATLAB

function tmp = code(s, u)
	tmp = (single(3.0) * s) * log((single(1.0) / (single(1.0) - ((u - single(0.25)) / single(0.75)))));
end

Initial Program: 95.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(3 \cdot s\right) \cdot \log \left(\frac{1}{1 - \frac{u - 0.25}{0.75}}\right) \end{array} \]

FPCore

(FPCore (s u)
 :precision binary32
 (* (* 3.0 s) (log (/ 1.0 (- 1.0 (/ (- u 0.25) 0.75))))))

C

float code(float s, float u) {
	return (3.0f * s) * logf((1.0f / (1.0f - ((u - 0.25f) / 0.75f))));
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = (3.0e0 * s) * log((1.0e0 / (1.0e0 - ((u - 0.25e0) / 0.75e0))))
end function

Julia

function code(s, u)
	return Float32(Float32(Float32(3.0) * s) * log(Float32(Float32(1.0) / Float32(Float32(1.0) - Float32(Float32(u - Float32(0.25)) / Float32(0.75))))))
end

MATLAB

function tmp = code(s, u)
	tmp = (single(3.0) * s) * log((single(1.0) / (single(1.0) - ((u - single(0.25)) / single(0.75)))));
end

Alternative 1: 98.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{\left(-u\right) - -0.25}{0.75}\right)\right) \end{array} \]

FPCore

(FPCore (s u)
 :precision binary32
 (* (* 3.0 s) (- (log1p (/ (- (- u) -0.25) 0.75)))))

C

float code(float s, float u) {
	return (3.0f * s) * -log1pf(((-u - -0.25f) / 0.75f));
}

Julia

function code(s, u)
	return Float32(Float32(Float32(3.0) * s) * Float32(-log1p(Float32(Float32(Float32(-u) - Float32(-0.25)) / Float32(0.75)))))
end

Derivation

1. Initial program 96.0%

   \[\left(3 \cdot s\right) \cdot \log \left(\frac{1}{1 - \frac{u - 0.25}{0.75}}\right) \]
2. Step-by-step derivation

   1. log-rec 96.6%

      \[\leadsto \left(3 \cdot s\right) \cdot \color{blue}{\left(-\log \left(1 - \frac{u - 0.25}{0.75}\right)\right)} \]

   2. sub-neg 96.6%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\log \color{blue}{\left(1 + \left(-\frac{u - 0.25}{0.75}\right)\right)}\right) \]

   3. log1p-def 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\color{blue}{\mathsf{log1p}\left(-\frac{u - 0.25}{0.75}\right)}\right) \]

   4. distribute-neg-frac 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\color{blue}{\frac{-\left(u - 0.25\right)}{0.75}}\right)\right) \]

   5. sub-neg 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{-\color{blue}{\left(u + \left(-0.25\right)\right)}}{0.75}\right)\right) \]

   6. metadata-eval 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{-\left(u + \color{blue}{-0.25}\right)}{0.75}\right)\right) \]

3. Simplified 98.3%

   \[\leadsto \color{blue}{\left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{-\left(u + -0.25\right)}{0.75}\right)\right)} \]

4. Final simplification 98.3%

   \[\leadsto \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{\left(-u\right) - -0.25}{0.75}\right)\right) \]

Alternative 2: 96.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ -3 \cdot \left(s \cdot \log \left(1.3333333333333333 - u \cdot 1.3333333333333333\right)\right) \end{array} \]

FPCore

(FPCore (s u)
 :precision binary32
 (* -3.0 (* s (log (- 1.3333333333333333 (* u 1.3333333333333333))))))

C

float code(float s, float u) {
	return -3.0f * (s * logf((1.3333333333333333f - (u * 1.3333333333333333f))));
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = (-3.0e0) * (s * log((1.3333333333333333e0 - (u * 1.3333333333333333e0))))
end function

Julia

function code(s, u)
	return Float32(Float32(-3.0) * Float32(s * log(Float32(Float32(1.3333333333333333) - Float32(u * Float32(1.3333333333333333))))))
end

MATLAB

function tmp = code(s, u)
	tmp = single(-3.0) * (s * log((single(1.3333333333333333) - (u * single(1.3333333333333333)))));
end

Derivation

1. Initial program 96.0%

   \[\left(3 \cdot s\right) \cdot \log \left(\frac{1}{1 - \frac{u - 0.25}{0.75}}\right) \]
2. Step-by-step derivation

   1. associate-*l* 95.9%

      \[\leadsto \color{blue}{3 \cdot \left(s \cdot \log \left(\frac{1}{1 - \frac{u - 0.25}{0.75}}\right)\right)} \]

   2. log-rec 96.6%

      \[\leadsto 3 \cdot \left(s \cdot \color{blue}{\left(-\log \left(1 - \frac{u - 0.25}{0.75}\right)\right)}\right) \]

   3. div-sub 95.8%

      \[\leadsto 3 \cdot \left(s \cdot \left(-\log \left(1 - \color{blue}{\left(\frac{u}{0.75} - \frac{0.25}{0.75}\right)}\right)\right)\right) \]

   4. metadata-eval 95.8%

      \[\leadsto 3 \cdot \left(s \cdot \left(-\log \left(1 - \left(\frac{u}{0.75} - \color{blue}{0.3333333333333333}\right)\right)\right)\right) \]

3. Simplified 95.8%

   \[\leadsto \color{blue}{3 \cdot \left(s \cdot \left(-\log \left(1 - \left(\frac{u}{0.75} - 0.3333333333333333\right)\right)\right)\right)} \]

4. Taylor expanded in s around 0 96.2%

   \[\leadsto \color{blue}{-3 \cdot \left(s \cdot \log \left(1.3333333333333333 - 1.3333333333333333 \cdot u\right)\right)} \]

5. Final simplification 96.2%

   \[\leadsto -3 \cdot \left(s \cdot \log \left(1.3333333333333333 - u \cdot 1.3333333333333333\right)\right) \]

Alternative 3: 97.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ -3 \cdot \left(s \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right) \end{array} \]

FPCore

(FPCore (s u)
 :precision binary32
 (* -3.0 (* s (log1p (* 1.3333333333333333 (- 0.25 u))))))

C

float code(float s, float u) {
	return -3.0f * (s * log1pf((1.3333333333333333f * (0.25f - u))));
}

Julia

function code(s, u)
	return Float32(Float32(-3.0) * Float32(s * log1p(Float32(Float32(1.3333333333333333) * Float32(Float32(0.25) - u)))))
end

Derivation

1. Initial program 96.0%

   \[\left(3 \cdot s\right) \cdot \log \left(\frac{1}{1 - \frac{u - 0.25}{0.75}}\right) \]
2. Step-by-step derivation

   1. log-rec 96.6%

      \[\leadsto \left(3 \cdot s\right) \cdot \color{blue}{\left(-\log \left(1 - \frac{u - 0.25}{0.75}\right)\right)} \]

   2. sub-neg 96.6%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\log \color{blue}{\left(1 + \left(-\frac{u - 0.25}{0.75}\right)\right)}\right) \]

   3. log1p-def 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\color{blue}{\mathsf{log1p}\left(-\frac{u - 0.25}{0.75}\right)}\right) \]

   4. distribute-neg-frac 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\color{blue}{\frac{-\left(u - 0.25\right)}{0.75}}\right)\right) \]

   5. sub-neg 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{-\color{blue}{\left(u + \left(-0.25\right)\right)}}{0.75}\right)\right) \]

   6. metadata-eval 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{-\left(u + \color{blue}{-0.25}\right)}{0.75}\right)\right) \]

3. Simplified 98.3%

   \[\leadsto \color{blue}{\left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{-\left(u + -0.25\right)}{0.75}\right)\right)} \]

4. Taylor expanded in s around 0 96.6%

   \[\leadsto \color{blue}{-3 \cdot \left(s \cdot \log \left(1 + 1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right)} \]
5. Step-by-step derivation

   1. log1p-def 97.8%

      \[\leadsto -3 \cdot \left(s \cdot \color{blue}{\mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)}\right) \]

6. Simplified 97.8%

   \[\leadsto \color{blue}{-3 \cdot \left(s \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right)} \]

7. Final simplification 97.8%

   \[\leadsto -3 \cdot \left(s \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right) \]

Alternative 4: 97.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ s \cdot \left(-3 \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right) \end{array} \]

FPCore

(FPCore (s u)
 :precision binary32
 (* s (* -3.0 (log1p (* 1.3333333333333333 (- 0.25 u))))))

C

float code(float s, float u) {
	return s * (-3.0f * log1pf((1.3333333333333333f * (0.25f - u))));
}

Julia

function code(s, u)
	return Float32(s * Float32(Float32(-3.0) * log1p(Float32(Float32(1.3333333333333333) * Float32(Float32(0.25) - u)))))
end

Derivation

1. Initial program 96.0%

   \[\left(3 \cdot s\right) \cdot \log \left(\frac{1}{1 - \frac{u - 0.25}{0.75}}\right) \]
2. Step-by-step derivation

   1. log-rec 96.6%

      \[\leadsto \left(3 \cdot s\right) \cdot \color{blue}{\left(-\log \left(1 - \frac{u - 0.25}{0.75}\right)\right)} \]

   2. sub-neg 96.6%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\log \color{blue}{\left(1 + \left(-\frac{u - 0.25}{0.75}\right)\right)}\right) \]

   3. log1p-def 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\color{blue}{\mathsf{log1p}\left(-\frac{u - 0.25}{0.75}\right)}\right) \]

   4. distribute-neg-frac 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\color{blue}{\frac{-\left(u - 0.25\right)}{0.75}}\right)\right) \]

   5. sub-neg 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{-\color{blue}{\left(u + \left(-0.25\right)\right)}}{0.75}\right)\right) \]

   6. metadata-eval 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{-\left(u + \color{blue}{-0.25}\right)}{0.75}\right)\right) \]

3. Simplified 98.3%

   \[\leadsto \color{blue}{\left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{-\left(u + -0.25\right)}{0.75}\right)\right)} \]

4. Taylor expanded in s around 0 96.6%

   \[\leadsto \color{blue}{-3 \cdot \left(s \cdot \log \left(1 + 1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right)} \]
5. Step-by-step derivation

   1. log1p-def 97.8%

      \[\leadsto -3 \cdot \left(s \cdot \color{blue}{\mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)}\right) \]

6. Simplified 97.8%

   \[\leadsto \color{blue}{-3 \cdot \left(s \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right)} \]

7. Taylor expanded in s around 0 96.6%

   \[\leadsto \color{blue}{-3 \cdot \left(s \cdot \log \left(1 + 1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right)} \]
8. Step-by-step derivation

   1. *-commutative 96.6%

      \[\leadsto \color{blue}{\left(s \cdot \log \left(1 + 1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right) \cdot -3} \]

   2. log1p-def 97.8%

      \[\leadsto \left(s \cdot \color{blue}{\mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)}\right) \cdot -3 \]

   3. associate-*r* 97.9%

      \[\leadsto \color{blue}{s \cdot \left(\mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right) \cdot -3\right)} \]

   4. *-commutative 97.9%

      \[\leadsto s \cdot \color{blue}{\left(-3 \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right)} \]

9. Simplified 97.9%

   \[\leadsto \color{blue}{s \cdot \left(-3 \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right)} \]

10. Final simplification 97.9%

    \[\leadsto s \cdot \left(-3 \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right) \]

Alternative 5: 97.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(s \cdot -3\right) \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right) \end{array} \]

FPCore

(FPCore (s u)
 :precision binary32
 (* (* s -3.0) (log1p (* 1.3333333333333333 (- 0.25 u)))))

C

float code(float s, float u) {
	return (s * -3.0f) * log1pf((1.3333333333333333f * (0.25f - u)));
}

Julia

function code(s, u)
	return Float32(Float32(s * Float32(-3.0)) * log1p(Float32(Float32(1.3333333333333333) * Float32(Float32(0.25) - u))))
end

Derivation

1. Initial program 96.0%

   \[\left(3 \cdot s\right) \cdot \log \left(\frac{1}{1 - \frac{u - 0.25}{0.75}}\right) \]
2. Step-by-step derivation

   1. log-rec 96.6%

      \[\leadsto \left(3 \cdot s\right) \cdot \color{blue}{\left(-\log \left(1 - \frac{u - 0.25}{0.75}\right)\right)} \]

   2. sub-neg 96.6%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\log \color{blue}{\left(1 + \left(-\frac{u - 0.25}{0.75}\right)\right)}\right) \]

   3. log1p-def 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\color{blue}{\mathsf{log1p}\left(-\frac{u - 0.25}{0.75}\right)}\right) \]

   4. distribute-neg-frac 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\color{blue}{\frac{-\left(u - 0.25\right)}{0.75}}\right)\right) \]

   5. sub-neg 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{-\color{blue}{\left(u + \left(-0.25\right)\right)}}{0.75}\right)\right) \]

   6. metadata-eval 98.3%

      \[\leadsto \left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{-\left(u + \color{blue}{-0.25}\right)}{0.75}\right)\right) \]

3. Simplified 98.3%

   \[\leadsto \color{blue}{\left(3 \cdot s\right) \cdot \left(-\mathsf{log1p}\left(\frac{-\left(u + -0.25\right)}{0.75}\right)\right)} \]

4. Taylor expanded in s around 0 96.6%

   \[\leadsto \color{blue}{-3 \cdot \left(s \cdot \log \left(1 + 1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right)} \]
5. Step-by-step derivation

   1. log1p-def 97.8%

      \[\leadsto -3 \cdot \left(s \cdot \color{blue}{\mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)}\right) \]

6. Simplified 97.8%

   \[\leadsto \color{blue}{-3 \cdot \left(s \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right)} \]
7. Step-by-step derivation

   1. add-exp-log 91.3%

      \[\leadsto \color{blue}{e^{\log \left(-3 \cdot \left(s \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right)\right)}} \]

   2. *-commutative 91.3%

      \[\leadsto e^{\log \color{blue}{\left(\left(s \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right) \cdot -3\right)}} \]

   3. associate-*l* 91.3%

      \[\leadsto e^{\log \color{blue}{\left(s \cdot \left(\mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right) \cdot -3\right)\right)}} \]

8. Applied egg-rr 91.3%

   \[\leadsto \color{blue}{e^{\log \left(s \cdot \left(\mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right) \cdot -3\right)\right)}} \]
9. Step-by-step derivation

   1. rem-exp-log 97.9%

      \[\leadsto \color{blue}{s \cdot \left(\mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right) \cdot -3\right)} \]

   2. *-commutative 97.9%

      \[\leadsto s \cdot \color{blue}{\left(-3 \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)\right)} \]

   3. associate-*r* 98.0%

      \[\leadsto \color{blue}{\left(s \cdot -3\right) \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)} \]

   4. *-commutative 98.0%

      \[\leadsto \color{blue}{\left(-3 \cdot s\right)} \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right) \]

10. Applied egg-rr 98.0%

    \[\leadsto \color{blue}{\left(-3 \cdot s\right) \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right)} \]

11. Final simplification 98.0%

    \[\leadsto \left(s \cdot -3\right) \cdot \mathsf{log1p}\left(1.3333333333333333 \cdot \left(0.25 - u\right)\right) \]

Alternative 6: 28.1% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ -3 \cdot \left(s \cdot \log 0.75\right) \end{array} \]

FPCore

(FPCore (s u) :precision binary32 (* -3.0 (* s (log 0.75))))

C

float code(float s, float u) {
	return -3.0f * (s * logf(0.75f));
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = (-3.0e0) * (s * log(0.75e0))
end function

Julia

function code(s, u)
	return Float32(Float32(-3.0) * Float32(s * log(Float32(0.75))))
end

MATLAB

function tmp = code(s, u)
	tmp = single(-3.0) * (s * log(single(0.75)));
end

Derivation

1. Initial program 96.0%

   \[\left(3 \cdot s\right) \cdot \log \left(\frac{1}{1 - \frac{u - 0.25}{0.75}}\right) \]

2. Taylor expanded in u around 0 7.3%

   \[\leadsto \left(3 \cdot s\right) \cdot \color{blue}{\log 0.75} \]
3. Step-by-step derivation

   1. expm1-log1p-u 7.1%

      \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\left(3 \cdot s\right) \cdot \log 0.75\right)\right)} \]

   2. expm1-udef 9.3%

      \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(\left(3 \cdot s\right) \cdot \log 0.75\right)} - 1} \]

   3. add-sqr-sqrt 9.3%

      \[\leadsto e^{\mathsf{log1p}\left(\color{blue}{\left(\sqrt{3 \cdot s} \cdot \sqrt{3 \cdot s}\right)} \cdot \log 0.75\right)} - 1 \]

   4. sqrt-unprod 9.3%

      \[\leadsto e^{\mathsf{log1p}\left(\color{blue}{\sqrt{\left(3 \cdot s\right) \cdot \left(3 \cdot s\right)}} \cdot \log 0.75\right)} - 1 \]

   5. swap-sqr 9.3%

      \[\leadsto e^{\mathsf{log1p}\left(\sqrt{\color{blue}{\left(3 \cdot 3\right) \cdot \left(s \cdot s\right)}} \cdot \log 0.75\right)} - 1 \]

   6. metadata-eval 9.3%

      \[\leadsto e^{\mathsf{log1p}\left(\sqrt{\color{blue}{9} \cdot \left(s \cdot s\right)} \cdot \log 0.75\right)} - 1 \]

   7. metadata-eval 9.3%

      \[\leadsto e^{\mathsf{log1p}\left(\sqrt{\color{blue}{\left(-3 \cdot -3\right)} \cdot \left(s \cdot s\right)} \cdot \log 0.75\right)} - 1 \]

   8. swap-sqr 9.3%

      \[\leadsto e^{\mathsf{log1p}\left(\sqrt{\color{blue}{\left(-3 \cdot s\right) \cdot \left(-3 \cdot s\right)}} \cdot \log 0.75\right)} - 1 \]

   9. sqrt-unprod -0.0%

      \[\leadsto e^{\mathsf{log1p}\left(\color{blue}{\left(\sqrt{-3 \cdot s} \cdot \sqrt{-3 \cdot s}\right)} \cdot \log 0.75\right)} - 1 \]

   10. add-sqr-sqrt 16.0%

       \[\leadsto e^{\mathsf{log1p}\left(\color{blue}{\left(-3 \cdot s\right)} \cdot \log 0.75\right)} - 1 \]

4. Applied egg-rr 16.0%

   \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(\left(-3 \cdot s\right) \cdot \log 0.75\right)} - 1} \]
5. Step-by-step derivation

   1. expm1-def 28.0%

      \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\left(-3 \cdot s\right) \cdot \log 0.75\right)\right)} \]

   2. expm1-log1p 28.0%

      \[\leadsto \color{blue}{\left(-3 \cdot s\right) \cdot \log 0.75} \]

   3. associate-*l* 28.0%

      \[\leadsto \color{blue}{-3 \cdot \left(s \cdot \log 0.75\right)} \]

6. Simplified 28.0%

   \[\leadsto \color{blue}{-3 \cdot \left(s \cdot \log 0.75\right)} \]

7. Final simplification 28.0%

   \[\leadsto -3 \cdot \left(s \cdot \log 0.75\right) \]

Alternative 7: 7.4% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ s \cdot \log 0.421875 \end{array} \]

FPCore

(FPCore (s u) :precision binary32 (* s (log 0.421875)))

C

float code(float s, float u) {
	return s * logf(0.421875f);
}

Fortran

real(4) function code(s, u)
    real(4), intent (in) :: s
    real(4), intent (in) :: u
    code = s * log(0.421875e0)
end function

Julia

function code(s, u)
	return Float32(s * log(Float32(0.421875)))
end

MATLAB

function tmp = code(s, u)
	tmp = s * log(single(0.421875));
end

Derivation

1. Initial program 96.0%

   \[\left(3 \cdot s\right) \cdot \log \left(\frac{1}{1 - \frac{u - 0.25}{0.75}}\right) \]
2. Step-by-step derivation

   1. *-commutative 96.0%

      \[\leadsto \color{blue}{\left(s \cdot 3\right)} \cdot \log \left(\frac{1}{1 - \frac{u - 0.25}{0.75}}\right) \]

   2. associate-*l* 96.0%

      \[\leadsto \color{blue}{s \cdot \left(3 \cdot \log \left(\frac{1}{1 - \frac{u - 0.25}{0.75}}\right)\right)} \]

   3. log-rec 96.7%

      \[\leadsto s \cdot \left(3 \cdot \color{blue}{\left(-\log \left(1 - \frac{u - 0.25}{0.75}\right)\right)}\right) \]

   4. neg-mul-1 96.7%

      \[\leadsto s \cdot \left(3 \cdot \color{blue}{\left(-1 \cdot \log \left(1 - \frac{u - 0.25}{0.75}\right)\right)}\right) \]

   5. associate-*r* 96.7%

      \[\leadsto s \cdot \color{blue}{\left(\left(3 \cdot -1\right) \cdot \log \left(1 - \frac{u - 0.25}{0.75}\right)\right)} \]

   6. metadata-eval 96.7%

      \[\leadsto s \cdot \left(\color{blue}{-3} \cdot \log \left(1 - \frac{u - 0.25}{0.75}\right)\right) \]

   7. sub-neg 96.7%

      \[\leadsto s \cdot \left(-3 \cdot \log \color{blue}{\left(1 + \left(-\frac{u - 0.25}{0.75}\right)\right)}\right) \]

   8. log1p-def 98.3%

      \[\leadsto s \cdot \left(-3 \cdot \color{blue}{\mathsf{log1p}\left(-\frac{u - 0.25}{0.75}\right)}\right) \]

   9. neg-mul-1 98.3%

      \[\leadsto s \cdot \left(-3 \cdot \mathsf{log1p}\left(\color{blue}{-1 \cdot \frac{u - 0.25}{0.75}}\right)\right) \]

   10. associate-*r/ 98.3%

       \[\leadsto s \cdot \left(-3 \cdot \mathsf{log1p}\left(\color{blue}{\frac{-1 \cdot \left(u - 0.25\right)}{0.75}}\right)\right) \]

   11. associate-/l* 97.9%

       \[\leadsto s \cdot \left(-3 \cdot \mathsf{log1p}\left(\color{blue}{\frac{-1}{\frac{0.75}{u - 0.25}}}\right)\right) \]

   12. associate-/r/ 97.9%

       \[\leadsto s \cdot \left(-3 \cdot \mathsf{log1p}\left(\color{blue}{\frac{-1}{0.75} \cdot \left(u - 0.25\right)}\right)\right) \]

   13. sub-neg 97.9%

       \[\leadsto s \cdot \left(-3 \cdot \mathsf{log1p}\left(\frac{-1}{0.75} \cdot \color{blue}{\left(u + \left(-0.25\right)\right)}\right)\right) \]

   14. distribute-lft-in 97.0%

       \[\leadsto s \cdot \left(-3 \cdot \mathsf{log1p}\left(\color{blue}{\frac{-1}{0.75} \cdot u + \frac{-1}{0.75} \cdot \left(-0.25\right)}\right)\right) \]

   15. metadata-eval 97.0%

       \[\leadsto s \cdot \left(-3 \cdot \mathsf{log1p}\left(\color{blue}{-1.3333333333333333} \cdot u + \frac{-1}{0.75} \cdot \left(-0.25\right)\right)\right) \]

   16. metadata-eval 97.0%

       \[\leadsto s \cdot \left(-3 \cdot \mathsf{log1p}\left(-1.3333333333333333 \cdot u + \color{blue}{-1.3333333333333333} \cdot \left(-0.25\right)\right)\right) \]

   17. metadata-eval 97.0%

       \[\leadsto s \cdot \left(-3 \cdot \mathsf{log1p}\left(-1.3333333333333333 \cdot u + -1.3333333333333333 \cdot \color{blue}{-0.25}\right)\right) \]

   18. metadata-eval 97.0%

       \[\leadsto s \cdot \left(-3 \cdot \mathsf{log1p}\left(-1.3333333333333333 \cdot u + \color{blue}{0.3333333333333333}\right)\right) \]

3. Simplified 97.0%

   \[\leadsto \color{blue}{s \cdot \left(-3 \cdot \mathsf{log1p}\left(-1.3333333333333333 \cdot u + 0.3333333333333333\right)\right)} \]
4. Step-by-step derivation

   1. add-log-exp 96.7%

      \[\leadsto s \cdot \color{blue}{\log \left(e^{-3 \cdot \mathsf{log1p}\left(-1.3333333333333333 \cdot u + 0.3333333333333333\right)}\right)} \]

   2. *-commutative 96.7%

      \[\leadsto s \cdot \log \left(e^{\color{blue}{\mathsf{log1p}\left(-1.3333333333333333 \cdot u + 0.3333333333333333\right) \cdot -3}}\right) \]

   3. log1p-udef 95.5%

      \[\leadsto s \cdot \log \left(e^{\color{blue}{\log \left(1 + \left(-1.3333333333333333 \cdot u + 0.3333333333333333\right)\right)} \cdot -3}\right) \]

   4. exp-to-pow 95.6%

      \[\leadsto s \cdot \log \color{blue}{\left({\left(1 + \left(-1.3333333333333333 \cdot u + 0.3333333333333333\right)\right)}^{-3}\right)} \]

   5. +-commutative 95.6%

      \[\leadsto s \cdot \log \left({\left(1 + \color{blue}{\left(0.3333333333333333 + -1.3333333333333333 \cdot u\right)}\right)}^{-3}\right) \]

   6. associate-+r+ 96.2%

      \[\leadsto s \cdot \log \left({\color{blue}{\left(\left(1 + 0.3333333333333333\right) + -1.3333333333333333 \cdot u\right)}}^{-3}\right) \]

   7. metadata-eval 96.2%

      \[\leadsto s \cdot \log \left({\left(\color{blue}{1.3333333333333333} + -1.3333333333333333 \cdot u\right)}^{-3}\right) \]

   8. +-commutative 96.2%

      \[\leadsto s \cdot \log \left({\color{blue}{\left(-1.3333333333333333 \cdot u + 1.3333333333333333\right)}}^{-3}\right) \]

   9. *-commutative 96.2%

      \[\leadsto s \cdot \log \left({\left(\color{blue}{u \cdot -1.3333333333333333} + 1.3333333333333333\right)}^{-3}\right) \]

   10. fma-udef 96.9%

       \[\leadsto s \cdot \log \left({\color{blue}{\left(\mathsf{fma}\left(u, -1.3333333333333333, 1.3333333333333333\right)\right)}}^{-3}\right) \]

5. Applied egg-rr 96.9%

   \[\leadsto s \cdot \color{blue}{\log \left({\left(\mathsf{fma}\left(u, -1.3333333333333333, 1.3333333333333333\right)\right)}^{-3}\right)} \]

6. Taylor expanded in u around 0 7.3%

   \[\leadsto s \cdot \log \color{blue}{0.421875} \]

7. Final simplification 7.3%

   \[\leadsto s \cdot \log 0.421875 \]

Reproduce

herbie shell --seed 2023310 
(FPCore (s u)
  :name "Disney BSSRDF, sample scattering profile, upper"
  :precision binary32
  :pre (and (and (<= 0.0 s) (<= s 256.0)) (and (<= 0.25 u) (<= u 1.0)))
  (* (* 3.0 s) (log (/ 1.0 (- 1.0 (/ (- u 0.25) 0.75))))))