Trowbridge-Reitz Sample, near normal, slope_x

Percentage Accurate: 99.0% → 99.0%

Time: 11.2s

Alternatives: 10

Speedup: 1.0×

Specification

\[\left(\left(cosTheta\_i > 0.9999 \land cosTheta\_i \leq 1\right) \land \left(2.328306437 \cdot 10^{-10} \leq u1 \land u1 \leq 1\right)\right) \land \left(2.328306437 \cdot 10^{-10} \leq u2 \land u2 \leq 1\right)\]

Math

\[\begin{array}{l} \\ \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2)
 :precision binary32
 (* (sqrt (/ u1 (- 1.0 u1))) (cos (* 6.28318530718 u2))))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf((u1 / (1.0f - u1))) * cosf((6.28318530718f * u2));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt((u1 / (1.0e0 - u1))) * cos((6.28318530718e0 * u2))
end function

Julia

function code(cosTheta_i, u1, u2)
	return Float32(sqrt(Float32(u1 / Float32(Float32(1.0) - u1))) * cos(Float32(Float32(6.28318530718) * u2)))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt((u1 / (single(1.0) - u1))) * cos((single(6.28318530718) * u2));
end

Initial Program: 99.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2)
 :precision binary32
 (* (sqrt (/ u1 (- 1.0 u1))) (cos (* 6.28318530718 u2))))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf((u1 / (1.0f - u1))) * cosf((6.28318530718f * u2));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt((u1 / (1.0e0 - u1))) * cos((6.28318530718e0 * u2))
end function

Julia

function code(cosTheta_i, u1, u2)
	return Float32(sqrt(Float32(u1 / Float32(Float32(1.0) - u1))) * cos(Float32(Float32(6.28318530718) * u2)))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt((u1 / (single(1.0) - u1))) * cos((single(6.28318530718) * u2));
end

Alternative 1: 99.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 6.28318530718 \cdot u2 + 2\\ \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2\right) \cdot t\_0}{t\_0}\right) \end{array} \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2)
 :precision binary32
 (let* ((t_0 (+ (* 6.28318530718 u2) 2.0)))
   (* (sqrt (/ u1 (- 1.0 u1))) (cos (/ (* (* 6.28318530718 u2) t_0) t_0)))))

C

float code(float cosTheta_i, float u1, float u2) {
	float t_0 = (6.28318530718f * u2) + 2.0f;
	return sqrtf((u1 / (1.0f - u1))) * cosf((((6.28318530718f * u2) * t_0) / t_0));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    real(4) :: t_0
    t_0 = (6.28318530718e0 * u2) + 2.0e0
    code = sqrt((u1 / (1.0e0 - u1))) * cos((((6.28318530718e0 * u2) * t_0) / t_0))
end function

Julia

function code(cosTheta_i, u1, u2)
	t_0 = Float32(Float32(Float32(6.28318530718) * u2) + Float32(2.0))
	return Float32(sqrt(Float32(u1 / Float32(Float32(1.0) - u1))) * cos(Float32(Float32(Float32(Float32(6.28318530718) * u2) * t_0) / t_0)))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	t_0 = (single(6.28318530718) * u2) + single(2.0);
	tmp = sqrt((u1 / (single(1.0) - u1))) * cos((((single(6.28318530718) * u2) * t_0) / t_0));
end

Derivation

1. Initial program 98.8%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. expm1-log1p-u 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(\mathsf{expm1}\left(\mathsf{log1p}\left(6.28318530718 \cdot u2\right)\right)\right)} \]

   2. expm1-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} - 1\right)} \]

4. Applied egg-rr 98.8%

   \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} - 1\right)} \]
5. Step-by-step derivation

   1. expm1-define 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(\mathsf{expm1}\left(\mathsf{log1p}\left(6.28318530718 \cdot u2\right)\right)\right)} \]

6. Simplified 98.8%

   \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(\mathsf{expm1}\left(\mathsf{log1p}\left(6.28318530718 \cdot u2\right)\right)\right)} \]
7. Step-by-step derivation

   1. expm1-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} - 1\right)} \]

   2. flip-- 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(\frac{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} \cdot e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} - 1 \cdot 1}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right)} \]

   3. metadata-eval 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} \cdot e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} - \color{blue}{1}}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   4. fmm-def 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\color{blue}{\mathsf{fma}\left(e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)}, e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)}, -1\right)}}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   5. log1p-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(e^{\color{blue}{\log \left(1 + 6.28318530718 \cdot u2\right)}}, e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   6. rem-exp-log 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\color{blue}{1 + 6.28318530718 \cdot u2}, e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   7. +-commutative 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\color{blue}{6.28318530718 \cdot u2 + 1}, e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   8. fma-define 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(6.28318530718, u2, 1\right)}, e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   9. log1p-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), e^{\color{blue}{\log \left(1 + 6.28318530718 \cdot u2\right)}}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   10. rem-exp-log 98.7%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \color{blue}{1 + 6.28318530718 \cdot u2}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   11. +-commutative 98.7%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \color{blue}{6.28318530718 \cdot u2 + 1}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   12. fma-define 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \color{blue}{\mathsf{fma}\left(6.28318530718, u2, 1\right)}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   13. metadata-eval 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \mathsf{fma}\left(6.28318530718, u2, 1\right), \color{blue}{-1}\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   14. log1p-undefine 98.7%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \mathsf{fma}\left(6.28318530718, u2, 1\right), -1\right)}{e^{\color{blue}{\log \left(1 + 6.28318530718 \cdot u2\right)}} + 1}\right) \]

   15. rem-exp-log 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \mathsf{fma}\left(6.28318530718, u2, 1\right), -1\right)}{\color{blue}{\left(1 + 6.28318530718 \cdot u2\right)} + 1}\right) \]

   16. +-commutative 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \mathsf{fma}\left(6.28318530718, u2, 1\right), -1\right)}{\color{blue}{\left(6.28318530718 \cdot u2 + 1\right)} + 1}\right) \]

   17. fma-define 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \mathsf{fma}\left(6.28318530718, u2, 1\right), -1\right)}{\color{blue}{\mathsf{fma}\left(6.28318530718, u2, 1\right)} + 1}\right) \]

8. Applied egg-rr 98.8%

   \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \mathsf{fma}\left(6.28318530718, u2, 1\right), -1\right)}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right)} \]
9. Step-by-step derivation

   1. fma-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\color{blue}{\mathsf{fma}\left(6.28318530718, u2, 1\right) \cdot \mathsf{fma}\left(6.28318530718, u2, 1\right) + -1}}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   2. difference-of-sqr--1 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\color{blue}{\left(\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1\right) \cdot \left(\mathsf{fma}\left(6.28318530718, u2, 1\right) - 1\right)}}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   3. fma-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(\color{blue}{\left(6.28318530718 \cdot u2 + 1\right)} + 1\right) \cdot \left(\mathsf{fma}\left(6.28318530718, u2, 1\right) - 1\right)}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   4. associate-+l+ 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\color{blue}{\left(6.28318530718 \cdot u2 + \left(1 + 1\right)\right)} \cdot \left(\mathsf{fma}\left(6.28318530718, u2, 1\right) - 1\right)}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   5. metadata-eval 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + \color{blue}{2}\right) \cdot \left(\mathsf{fma}\left(6.28318530718, u2, 1\right) - 1\right)}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   6. fma-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \left(\color{blue}{\left(6.28318530718 \cdot u2 + 1\right)} - 1\right)}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   7. associate--l+ 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \color{blue}{\left(6.28318530718 \cdot u2 + \left(1 - 1\right)\right)}}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   8. metadata-eval 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \left(6.28318530718 \cdot u2 + \color{blue}{0}\right)}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   9. +-rgt-identity 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \color{blue}{\left(6.28318530718 \cdot u2\right)}}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   10. fma-undefine 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \left(6.28318530718 \cdot u2\right)}{\color{blue}{\left(6.28318530718 \cdot u2 + 1\right)} + 1}\right) \]

   11. associate-+l+ 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \left(6.28318530718 \cdot u2\right)}{\color{blue}{6.28318530718 \cdot u2 + \left(1 + 1\right)}}\right) \]

   12. metadata-eval 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \left(6.28318530718 \cdot u2\right)}{6.28318530718 \cdot u2 + \color{blue}{2}}\right) \]

10. Simplified 98.8%

    \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \left(6.28318530718 \cdot u2\right)}{6.28318530718 \cdot u2 + 2}\right)} \]

11. Final simplification 98.8%

    \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2\right) \cdot \left(6.28318530718 \cdot u2 + 2\right)}{6.28318530718 \cdot u2 + 2}\right) \]
12. Add Preprocessing

Alternative 2: 99.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{u2 \cdot \left(12.56637061436 + u2 \cdot 39.47841760436263\right)}{6.28318530718 \cdot u2 + 2}\right) \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2)
 :precision binary32
 (*
  (sqrt (/ u1 (- 1.0 u1)))
  (cos
   (/
    (* u2 (+ 12.56637061436 (* u2 39.47841760436263)))
    (+ (* 6.28318530718 u2) 2.0)))))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf((u1 / (1.0f - u1))) * cosf(((u2 * (12.56637061436f + (u2 * 39.47841760436263f))) / ((6.28318530718f * u2) + 2.0f)));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt((u1 / (1.0e0 - u1))) * cos(((u2 * (12.56637061436e0 + (u2 * 39.47841760436263e0))) / ((6.28318530718e0 * u2) + 2.0e0)))
end function

Julia

function code(cosTheta_i, u1, u2)
	return Float32(sqrt(Float32(u1 / Float32(Float32(1.0) - u1))) * cos(Float32(Float32(u2 * Float32(Float32(12.56637061436) + Float32(u2 * Float32(39.47841760436263)))) / Float32(Float32(Float32(6.28318530718) * u2) + Float32(2.0)))))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt((u1 / (single(1.0) - u1))) * cos(((u2 * (single(12.56637061436) + (u2 * single(39.47841760436263)))) / ((single(6.28318530718) * u2) + single(2.0))));
end

Derivation

1. Initial program 98.8%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. expm1-log1p-u 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(\mathsf{expm1}\left(\mathsf{log1p}\left(6.28318530718 \cdot u2\right)\right)\right)} \]

   2. expm1-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} - 1\right)} \]

4. Applied egg-rr 98.8%

   \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} - 1\right)} \]
5. Step-by-step derivation

   1. expm1-define 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(\mathsf{expm1}\left(\mathsf{log1p}\left(6.28318530718 \cdot u2\right)\right)\right)} \]

6. Simplified 98.8%

   \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(\mathsf{expm1}\left(\mathsf{log1p}\left(6.28318530718 \cdot u2\right)\right)\right)} \]
7. Step-by-step derivation

   1. expm1-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} - 1\right)} \]

   2. flip-- 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(\frac{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} \cdot e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} - 1 \cdot 1}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right)} \]

   3. metadata-eval 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} \cdot e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} - \color{blue}{1}}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   4. fmm-def 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\color{blue}{\mathsf{fma}\left(e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)}, e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)}, -1\right)}}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   5. log1p-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(e^{\color{blue}{\log \left(1 + 6.28318530718 \cdot u2\right)}}, e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   6. rem-exp-log 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\color{blue}{1 + 6.28318530718 \cdot u2}, e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   7. +-commutative 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\color{blue}{6.28318530718 \cdot u2 + 1}, e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   8. fma-define 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(6.28318530718, u2, 1\right)}, e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   9. log1p-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), e^{\color{blue}{\log \left(1 + 6.28318530718 \cdot u2\right)}}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   10. rem-exp-log 98.7%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \color{blue}{1 + 6.28318530718 \cdot u2}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   11. +-commutative 98.7%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \color{blue}{6.28318530718 \cdot u2 + 1}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   12. fma-define 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \color{blue}{\mathsf{fma}\left(6.28318530718, u2, 1\right)}, -1\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   13. metadata-eval 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \mathsf{fma}\left(6.28318530718, u2, 1\right), \color{blue}{-1}\right)}{e^{\mathsf{log1p}\left(6.28318530718 \cdot u2\right)} + 1}\right) \]

   14. log1p-undefine 98.7%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \mathsf{fma}\left(6.28318530718, u2, 1\right), -1\right)}{e^{\color{blue}{\log \left(1 + 6.28318530718 \cdot u2\right)}} + 1}\right) \]

   15. rem-exp-log 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \mathsf{fma}\left(6.28318530718, u2, 1\right), -1\right)}{\color{blue}{\left(1 + 6.28318530718 \cdot u2\right)} + 1}\right) \]

   16. +-commutative 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \mathsf{fma}\left(6.28318530718, u2, 1\right), -1\right)}{\color{blue}{\left(6.28318530718 \cdot u2 + 1\right)} + 1}\right) \]

   17. fma-define 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \mathsf{fma}\left(6.28318530718, u2, 1\right), -1\right)}{\color{blue}{\mathsf{fma}\left(6.28318530718, u2, 1\right)} + 1}\right) \]

8. Applied egg-rr 98.8%

   \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(\frac{\mathsf{fma}\left(\mathsf{fma}\left(6.28318530718, u2, 1\right), \mathsf{fma}\left(6.28318530718, u2, 1\right), -1\right)}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right)} \]
9. Step-by-step derivation

   1. fma-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\color{blue}{\mathsf{fma}\left(6.28318530718, u2, 1\right) \cdot \mathsf{fma}\left(6.28318530718, u2, 1\right) + -1}}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   2. difference-of-sqr--1 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\color{blue}{\left(\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1\right) \cdot \left(\mathsf{fma}\left(6.28318530718, u2, 1\right) - 1\right)}}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   3. fma-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(\color{blue}{\left(6.28318530718 \cdot u2 + 1\right)} + 1\right) \cdot \left(\mathsf{fma}\left(6.28318530718, u2, 1\right) - 1\right)}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   4. associate-+l+ 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\color{blue}{\left(6.28318530718 \cdot u2 + \left(1 + 1\right)\right)} \cdot \left(\mathsf{fma}\left(6.28318530718, u2, 1\right) - 1\right)}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   5. metadata-eval 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + \color{blue}{2}\right) \cdot \left(\mathsf{fma}\left(6.28318530718, u2, 1\right) - 1\right)}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   6. fma-undefine 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \left(\color{blue}{\left(6.28318530718 \cdot u2 + 1\right)} - 1\right)}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   7. associate--l+ 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \color{blue}{\left(6.28318530718 \cdot u2 + \left(1 - 1\right)\right)}}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   8. metadata-eval 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \left(6.28318530718 \cdot u2 + \color{blue}{0}\right)}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   9. +-rgt-identity 98.8%

      \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \color{blue}{\left(6.28318530718 \cdot u2\right)}}{\mathsf{fma}\left(6.28318530718, u2, 1\right) + 1}\right) \]

   10. fma-undefine 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \left(6.28318530718 \cdot u2\right)}{\color{blue}{\left(6.28318530718 \cdot u2 + 1\right)} + 1}\right) \]

   11. associate-+l+ 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \left(6.28318530718 \cdot u2\right)}{\color{blue}{6.28318530718 \cdot u2 + \left(1 + 1\right)}}\right) \]

   12. metadata-eval 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \left(6.28318530718 \cdot u2\right)}{6.28318530718 \cdot u2 + \color{blue}{2}}\right) \]

10. Simplified 98.8%

    \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \color{blue}{\left(\frac{\left(6.28318530718 \cdot u2 + 2\right) \cdot \left(6.28318530718 \cdot u2\right)}{6.28318530718 \cdot u2 + 2}\right)} \]

11. Taylor expanded in u2 around 0 98.8%

    \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\color{blue}{u2 \cdot \left(12.56637061436 + 39.47841760436263 \cdot u2\right)}}{6.28318530718 \cdot u2 + 2}\right) \]
12. Step-by-step derivation

    1. *-commutative 98.8%

       \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{u2 \cdot \left(12.56637061436 + \color{blue}{u2 \cdot 39.47841760436263}\right)}{6.28318530718 \cdot u2 + 2}\right) \]

13. Simplified 98.8%

    \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(\frac{\color{blue}{u2 \cdot \left(12.56637061436 + u2 \cdot 39.47841760436263\right)}}{6.28318530718 \cdot u2 + 2}\right) \]
14. Add Preprocessing

Alternative 3: 94.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;6.28318530718 \cdot u2 \leq 0.0006200000061653554:\\ \;\;\;\;\sqrt{\frac{u1}{1 - u1}}\\ \mathbf{else}:\\ \;\;\;\;\cos \left(6.28318530718 \cdot u2\right) \cdot \sqrt{u1 \cdot \left(u1 + 1\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2)
 :precision binary32
 (if (<= (* 6.28318530718 u2) 0.0006200000061653554)
   (sqrt (/ u1 (- 1.0 u1)))
   (* (cos (* 6.28318530718 u2)) (sqrt (* u1 (+ u1 1.0))))))

C

float code(float cosTheta_i, float u1, float u2) {
	float tmp;
	if ((6.28318530718f * u2) <= 0.0006200000061653554f) {
		tmp = sqrtf((u1 / (1.0f - u1)));
	} else {
		tmp = cosf((6.28318530718f * u2)) * sqrtf((u1 * (u1 + 1.0f)));
	}
	return tmp;
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    real(4) :: tmp
    if ((6.28318530718e0 * u2) <= 0.0006200000061653554e0) then
        tmp = sqrt((u1 / (1.0e0 - u1)))
    else
        tmp = cos((6.28318530718e0 * u2)) * sqrt((u1 * (u1 + 1.0e0)))
    end if
    code = tmp
end function

Julia

function code(cosTheta_i, u1, u2)
	tmp = Float32(0.0)
	if (Float32(Float32(6.28318530718) * u2) <= Float32(0.0006200000061653554))
		tmp = sqrt(Float32(u1 / Float32(Float32(1.0) - u1)));
	else
		tmp = Float32(cos(Float32(Float32(6.28318530718) * u2)) * sqrt(Float32(u1 * Float32(u1 + Float32(1.0)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(cosTheta_i, u1, u2)
	tmp = single(0.0);
	if ((single(6.28318530718) * u2) <= single(0.0006200000061653554))
		tmp = sqrt((u1 / (single(1.0) - u1)));
	else
		tmp = cos((single(6.28318530718) * u2)) * sqrt((u1 * (u1 + single(1.0))));
	end
	tmp_2 = tmp;
end

Derivation

1. Split input into 2 regimes

2. if (*.f32 #s(literal 314159265359/50000000000 binary32) u2) < 6.20000006e-4

   1. Initial program 99.5%

      \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
   2. Add Preprocessing

   3. Taylor expanded in u2 around 0 99.4%

      \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}}} \]

   if 6.20000006e-4 < (*.f32 #s(literal 314159265359/50000000000 binary32) u2)

   1. Initial program 98.0%

      \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
   2. Add Preprocessing

   3. Taylor expanded in u1 around 0 83.7%

      \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(1 + u1\right)}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
   4. Step-by-step derivation

      1. +-commutative 83.7%

         \[\leadsto \sqrt{u1 \cdot \color{blue}{\left(u1 + 1\right)}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

   5. Simplified 83.7%

      \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(u1 + 1\right)}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 92.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;6.28318530718 \cdot u2 \leq 0.0006200000061653554:\\ \;\;\;\;\sqrt{\frac{u1}{1 - u1}}\\ \mathbf{else}:\\ \;\;\;\;\cos \left(6.28318530718 \cdot u2\right) \cdot \sqrt{u1 \cdot \left(u1 + 1\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 90.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;6.28318530718 \cdot u2 \leq 0.014999999664723873:\\ \;\;\;\;\sqrt{\frac{u1}{1 - u1}}\\ \mathbf{else}:\\ \;\;\;\;\cos \left(6.28318530718 \cdot u2\right) \cdot \sqrt{u1}\\ \end{array} \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2)
 :precision binary32
 (if (<= (* 6.28318530718 u2) 0.014999999664723873)
   (sqrt (/ u1 (- 1.0 u1)))
   (* (cos (* 6.28318530718 u2)) (sqrt u1))))

C

float code(float cosTheta_i, float u1, float u2) {
	float tmp;
	if ((6.28318530718f * u2) <= 0.014999999664723873f) {
		tmp = sqrtf((u1 / (1.0f - u1)));
	} else {
		tmp = cosf((6.28318530718f * u2)) * sqrtf(u1);
	}
	return tmp;
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    real(4) :: tmp
    if ((6.28318530718e0 * u2) <= 0.014999999664723873e0) then
        tmp = sqrt((u1 / (1.0e0 - u1)))
    else
        tmp = cos((6.28318530718e0 * u2)) * sqrt(u1)
    end if
    code = tmp
end function

Julia

function code(cosTheta_i, u1, u2)
	tmp = Float32(0.0)
	if (Float32(Float32(6.28318530718) * u2) <= Float32(0.014999999664723873))
		tmp = sqrt(Float32(u1 / Float32(Float32(1.0) - u1)));
	else
		tmp = Float32(cos(Float32(Float32(6.28318530718) * u2)) * sqrt(u1));
	end
	return tmp
end

MATLAB

function tmp_2 = code(cosTheta_i, u1, u2)
	tmp = single(0.0);
	if ((single(6.28318530718) * u2) <= single(0.014999999664723873))
		tmp = sqrt((u1 / (single(1.0) - u1)));
	else
		tmp = cos((single(6.28318530718) * u2)) * sqrt(u1);
	end
	tmp_2 = tmp;
end

Derivation

1. Split input into 2 regimes

2. if (*.f32 #s(literal 314159265359/50000000000 binary32) u2) < 0.0149999997

   1. Initial program 99.4%

      \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
   2. Add Preprocessing

   3. Taylor expanded in u2 around 0 96.2%

      \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}}} \]

   if 0.0149999997 < (*.f32 #s(literal 314159265359/50000000000 binary32) u2)

   1. Initial program 97.6%

      \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
   2. Add Preprocessing

   3. Taylor expanded in u1 around 0 69.8%

      \[\leadsto \color{blue}{\sqrt{u1} \cdot \cos \left(6.28318530718 \cdot u2\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 88.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;6.28318530718 \cdot u2 \leq 0.014999999664723873:\\ \;\;\;\;\sqrt{\frac{u1}{1 - u1}}\\ \mathbf{else}:\\ \;\;\;\;\cos \left(6.28318530718 \cdot u2\right) \cdot \sqrt{u1}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 99.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2)
 :precision binary32
 (* (sqrt (/ u1 (- 1.0 u1))) (cos (* 6.28318530718 u2))))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf((u1 / (1.0f - u1))) * cosf((6.28318530718f * u2));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt((u1 / (1.0e0 - u1))) * cos((6.28318530718e0 * u2))
end function

Julia

function code(cosTheta_i, u1, u2)
	return Float32(sqrt(Float32(u1 / Float32(Float32(1.0) - u1))) * cos(Float32(Float32(6.28318530718) * u2)))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt((u1 / (single(1.0) - u1))) * cos((single(6.28318530718) * u2));
end

Derivation

1. Initial program 98.8%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 6: 80.6% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{\frac{u1}{1 - u1}} \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2) :precision binary32 (sqrt (/ u1 (- 1.0 u1))))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf((u1 / (1.0f - u1)));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt((u1 / (1.0e0 - u1)))
end function

Julia

function code(cosTheta_i, u1, u2)
	return sqrt(Float32(u1 / Float32(Float32(1.0) - u1)))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt((u1 / (single(1.0) - u1)));
end

Derivation

1. Initial program 98.8%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing

3. Taylor expanded in u2 around 0 78.8%

   \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}}} \]
4. Add Preprocessing

Alternative 7: 71.9% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{u1 \cdot \left(u1 + 1\right)} \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2) :precision binary32 (sqrt (* u1 (+ u1 1.0))))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf((u1 * (u1 + 1.0f)));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt((u1 * (u1 + 1.0e0)))
end function

Julia

function code(cosTheta_i, u1, u2)
	return sqrt(Float32(u1 * Float32(u1 + Float32(1.0))))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt((u1 * (u1 + single(1.0))));
end

Derivation

1. Initial program 98.8%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing

3. Taylor expanded in u2 around 0 78.8%

   \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}}} \]

4. Taylor expanded in u1 around 0 72.6%

   \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(1 + u1\right)}} \]
5. Step-by-step derivation

   1. +-commutative 87.0%

      \[\leadsto \sqrt{u1 \cdot \color{blue}{\left(u1 + 1\right)}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

6. Simplified 72.6%

   \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(u1 + 1\right)}} \]
7. Add Preprocessing

Alternative 8: 63.5% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \sqrt{u1} \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2) :precision binary32 (sqrt u1))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf(u1);
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt(u1)
end function

Julia

function code(cosTheta_i, u1, u2)
	return sqrt(u1)
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt(u1);
end

Derivation

1. Initial program 98.8%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing

3. Taylor expanded in u2 around 0 78.8%

   \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}}} \]

4. Taylor expanded in u1 around 0 64.1%

   \[\leadsto \color{blue}{\sqrt{u1}} \]
5. Add Preprocessing

Alternative 9: 20.2% accurate, 29.9× speedup

Math

\[\begin{array}{l} \\ u1 \cdot \left(1 + \frac{0.5}{u1}\right) \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2) :precision binary32 (* u1 (+ 1.0 (/ 0.5 u1))))

C

float code(float cosTheta_i, float u1, float u2) {
	return u1 * (1.0f + (0.5f / u1));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = u1 * (1.0e0 + (0.5e0 / u1))
end function

Julia

function code(cosTheta_i, u1, u2)
	return Float32(u1 * Float32(Float32(1.0) + Float32(Float32(0.5) / u1)))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = u1 * (single(1.0) + (single(0.5) / u1));
end

Derivation

1. Initial program 98.8%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing

3. Taylor expanded in u2 around 0 78.8%

   \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}}} \]

4. Taylor expanded in u1 around 0 72.6%

   \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(1 + u1\right)}} \]
5. Step-by-step derivation

   1. +-commutative 87.0%

      \[\leadsto \sqrt{u1 \cdot \color{blue}{\left(u1 + 1\right)}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

6. Simplified 72.6%

   \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(u1 + 1\right)}} \]

7. Taylor expanded in u1 around inf 19.7%

   \[\leadsto \color{blue}{u1 \cdot \left(1 + 0.5 \cdot \frac{1}{u1}\right)} \]
8. Step-by-step derivation

   1. associate-*r/ 19.7%

      \[\leadsto u1 \cdot \left(1 + \color{blue}{\frac{0.5 \cdot 1}{u1}}\right) \]

   2. metadata-eval 19.7%

      \[\leadsto u1 \cdot \left(1 + \frac{\color{blue}{0.5}}{u1}\right) \]

9. Simplified 19.7%

   \[\leadsto \color{blue}{u1 \cdot \left(1 + \frac{0.5}{u1}\right)} \]
10. Add Preprocessing

Alternative 10: 20.2% accurate, 69.7× speedup

Math

\[\begin{array}{l} \\ u1 + 0.5 \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2) :precision binary32 (+ u1 0.5))

C

float code(float cosTheta_i, float u1, float u2) {
	return u1 + 0.5f;
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = u1 + 0.5e0
end function

Julia

function code(cosTheta_i, u1, u2)
	return Float32(u1 + Float32(0.5))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = u1 + single(0.5);
end

Derivation

1. Initial program 98.8%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing

3. Taylor expanded in u2 around 0 78.8%

   \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}}} \]

4. Taylor expanded in u1 around 0 72.6%

   \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(1 + u1\right)}} \]
5. Step-by-step derivation

   1. +-commutative 87.0%

      \[\leadsto \sqrt{u1 \cdot \color{blue}{\left(u1 + 1\right)}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

6. Simplified 72.6%

   \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(u1 + 1\right)}} \]

7. Taylor expanded in u1 around inf 19.7%

   \[\leadsto \color{blue}{u1 \cdot \left(1 + 0.5 \cdot \frac{1}{u1}\right)} \]
8. Step-by-step derivation

   1. distribute-rgt-in 19.7%

      \[\leadsto \color{blue}{1 \cdot u1 + \left(0.5 \cdot \frac{1}{u1}\right) \cdot u1} \]

   2. *-lft-identity 19.7%

      \[\leadsto \color{blue}{u1} + \left(0.5 \cdot \frac{1}{u1}\right) \cdot u1 \]

   3. associate-*l* 19.7%

      \[\leadsto u1 + \color{blue}{0.5 \cdot \left(\frac{1}{u1} \cdot u1\right)} \]

   4. lft-mult-inverse 19.7%

      \[\leadsto u1 + 0.5 \cdot \color{blue}{1} \]

   5. metadata-eval 19.7%

      \[\leadsto u1 + \color{blue}{0.5} \]

9. Simplified 19.7%

   \[\leadsto \color{blue}{u1 + 0.5} \]
10. Add Preprocessing

Reproduce

herbie shell --seed 2024151 
(FPCore (cosTheta_i u1 u2)
  :name "Trowbridge-Reitz Sample, near normal, slope_x"
  :precision binary32
  :pre (and (and (and (> cosTheta_i 0.9999) (<= cosTheta_i 1.0)) (and (<= 2.328306437e-10 u1) (<= u1 1.0))) (and (<= 2.328306437e-10 u2) (<= u2 1.0)))
  (* (sqrt (/ u1 (- 1.0 u1))) (cos (* 6.28318530718 u2))))