Quotient of products

Percentage Accurate: 86.3% → 91.6%

Time: 2.3s

Alternatives: 2

Speedup: 0.3×

Specification

Math

\[\begin{array}{l} \\ \frac{a1 \cdot a2}{b1 \cdot b2} \end{array} \]

FPCore

(FPCore (a1 a2 b1 b2) :precision binary64 (/ (* a1 a2) (* b1 b2)))

C

double code(double a1, double a2, double b1, double b2) {
	return (a1 * a2) / (b1 * b2);
}

Fortran

real(8) function code(a1, a2, b1, b2)
    real(8), intent (in) :: a1
    real(8), intent (in) :: a2
    real(8), intent (in) :: b1
    real(8), intent (in) :: b2
    code = (a1 * a2) / (b1 * b2)
end function

Java

public static double code(double a1, double a2, double b1, double b2) {
	return (a1 * a2) / (b1 * b2);
}

Python

def code(a1, a2, b1, b2):
	return (a1 * a2) / (b1 * b2)

Julia

function code(a1, a2, b1, b2)
	return Float64(Float64(a1 * a2) / Float64(b1 * b2))
end

MATLAB

function tmp = code(a1, a2, b1, b2)
	tmp = (a1 * a2) / (b1 * b2);
end

Wolfram

code[a1_, a2_, b1_, b2_] := N[(N[(a1 * a2), $MachinePrecision] / N[(b1 * b2), $MachinePrecision]), $MachinePrecision]

Initial Program: 86.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{a1 \cdot a2}{b1 \cdot b2} \end{array} \]

FPCore

(FPCore (a1 a2 b1 b2) :precision binary64 (/ (* a1 a2) (* b1 b2)))

C

double code(double a1, double a2, double b1, double b2) {
	return (a1 * a2) / (b1 * b2);
}

Fortran

real(8) function code(a1, a2, b1, b2)
    real(8), intent (in) :: a1
    real(8), intent (in) :: a2
    real(8), intent (in) :: b1
    real(8), intent (in) :: b2
    code = (a1 * a2) / (b1 * b2)
end function

Java

public static double code(double a1, double a2, double b1, double b2) {
	return (a1 * a2) / (b1 * b2);
}

Python

def code(a1, a2, b1, b2):
	return (a1 * a2) / (b1 * b2)

Julia

function code(a1, a2, b1, b2)
	return Float64(Float64(a1 * a2) / Float64(b1 * b2))
end

MATLAB

function tmp = code(a1, a2, b1, b2)
	tmp = (a1 * a2) / (b1 * b2);
end

Wolfram

code[a1_, a2_, b1_, b2_] := N[(N[(a1 * a2), $MachinePrecision] / N[(b1 * b2), $MachinePrecision]), $MachinePrecision]

Alternative 1: 91.6% accurate, 0.3× speedup

Math

\[\begin{array}{l} b2\_m = \left|b2\right| \\ b2\_s = \mathsf{copysign}\left(1, b2\right) \\ b1\_m = \left|b1\right| \\ b1\_s = \mathsf{copysign}\left(1, b1\right) \\ a2\_m = \left|a2\right| \\ a2\_s = \mathsf{copysign}\left(1, a2\right) \\ a1\_m = \left|a1\right| \\ a1\_s = \mathsf{copysign}\left(1, a1\right) \\ [a1_m, a2_m, b1_m, b2_m] = \mathsf{sort}([a1_m, a2_m, b1_m, b2_m])\\ \\ \begin{array}{l} t_0 := \frac{a1\_m \cdot a2\_m}{b1\_m \cdot b2\_m}\\ t_1 := a1\_m \cdot \frac{a2\_m}{b1\_m \cdot b2\_m}\\ a1\_s \cdot \left(a2\_s \cdot \left(b1\_s \cdot \left(b2\_s \cdot \begin{array}{l} \mathbf{if}\;t\_0 \leq 10:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_0 \leq 10^{+308}:\\ \;\;\;\;t\_0\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array}\right)\right)\right) \end{array} \end{array} \]

FPCore

b2\_m = (fabs.f64 b2)
b2\_s = (copysign.f64 #s(literal 1 binary64) b2)
b1\_m = (fabs.f64 b1)
b1\_s = (copysign.f64 #s(literal 1 binary64) b1)
a2\_m = (fabs.f64 a2)
a2\_s = (copysign.f64 #s(literal 1 binary64) a2)
a1\_m = (fabs.f64 a1)
a1\_s = (copysign.f64 #s(literal 1 binary64) a1)
NOTE: a1_m, a2_m, b1_m, and b2_m should be sorted in increasing order before calling this function.
(FPCore (a1_s a2_s b1_s b2_s a1_m a2_m b1_m b2_m)
 :precision binary64
 (let* ((t_0 (/ (* a1_m a2_m) (* b1_m b2_m)))
        (t_1 (* a1_m (/ a2_m (* b1_m b2_m)))))
   (*
    a1_s
    (*
     a2_s
     (* b1_s (* b2_s (if (<= t_0 10.0) t_1 (if (<= t_0 1e+308) t_0 t_1))))))))

C

b2\_m = fabs(b2);
b2\_s = copysign(1.0, b2);
b1\_m = fabs(b1);
b1\_s = copysign(1.0, b1);
a2\_m = fabs(a2);
a2\_s = copysign(1.0, a2);
a1\_m = fabs(a1);
a1\_s = copysign(1.0, a1);
assert(a1_m < a2_m && a2_m < b1_m && b1_m < b2_m);
double code(double a1_s, double a2_s, double b1_s, double b2_s, double a1_m, double a2_m, double b1_m, double b2_m) {
	double t_0 = (a1_m * a2_m) / (b1_m * b2_m);
	double t_1 = a1_m * (a2_m / (b1_m * b2_m));
	double tmp;
	if (t_0 <= 10.0) {
		tmp = t_1;
	} else if (t_0 <= 1e+308) {
		tmp = t_0;
	} else {
		tmp = t_1;
	}
	return a1_s * (a2_s * (b1_s * (b2_s * tmp)));
}

Fortran

b2\_m = abs(b2)
b2\_s = copysign(1.0d0, b2)
b1\_m = abs(b1)
b1\_s = copysign(1.0d0, b1)
a2\_m = abs(a2)
a2\_s = copysign(1.0d0, a2)
a1\_m = abs(a1)
a1\_s = copysign(1.0d0, a1)
NOTE: a1_m, a2_m, b1_m, and b2_m should be sorted in increasing order before calling this function.
real(8) function code(a1_s, a2_s, b1_s, b2_s, a1_m, a2_m, b1_m, b2_m)
    real(8), intent (in) :: a1_s
    real(8), intent (in) :: a2_s
    real(8), intent (in) :: b1_s
    real(8), intent (in) :: b2_s
    real(8), intent (in) :: a1_m
    real(8), intent (in) :: a2_m
    real(8), intent (in) :: b1_m
    real(8), intent (in) :: b2_m
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = (a1_m * a2_m) / (b1_m * b2_m)
    t_1 = a1_m * (a2_m / (b1_m * b2_m))
    if (t_0 <= 10.0d0) then
        tmp = t_1
    else if (t_0 <= 1d+308) then
        tmp = t_0
    else
        tmp = t_1
    end if
    code = a1_s * (a2_s * (b1_s * (b2_s * tmp)))
end function

Java

b2\_m = Math.abs(b2);
b2\_s = Math.copySign(1.0, b2);
b1\_m = Math.abs(b1);
b1\_s = Math.copySign(1.0, b1);
a2\_m = Math.abs(a2);
a2\_s = Math.copySign(1.0, a2);
a1\_m = Math.abs(a1);
a1\_s = Math.copySign(1.0, a1);
assert a1_m < a2_m && a2_m < b1_m && b1_m < b2_m;
public static double code(double a1_s, double a2_s, double b1_s, double b2_s, double a1_m, double a2_m, double b1_m, double b2_m) {
	double t_0 = (a1_m * a2_m) / (b1_m * b2_m);
	double t_1 = a1_m * (a2_m / (b1_m * b2_m));
	double tmp;
	if (t_0 <= 10.0) {
		tmp = t_1;
	} else if (t_0 <= 1e+308) {
		tmp = t_0;
	} else {
		tmp = t_1;
	}
	return a1_s * (a2_s * (b1_s * (b2_s * tmp)));
}

Python

b2\_m = math.fabs(b2)
b2\_s = math.copysign(1.0, b2)
b1\_m = math.fabs(b1)
b1\_s = math.copysign(1.0, b1)
a2\_m = math.fabs(a2)
a2\_s = math.copysign(1.0, a2)
a1\_m = math.fabs(a1)
a1\_s = math.copysign(1.0, a1)
[a1_m, a2_m, b1_m, b2_m] = sort([a1_m, a2_m, b1_m, b2_m])
def code(a1_s, a2_s, b1_s, b2_s, a1_m, a2_m, b1_m, b2_m):
	t_0 = (a1_m * a2_m) / (b1_m * b2_m)
	t_1 = a1_m * (a2_m / (b1_m * b2_m))
	tmp = 0
	if t_0 <= 10.0:
		tmp = t_1
	elif t_0 <= 1e+308:
		tmp = t_0
	else:
		tmp = t_1
	return a1_s * (a2_s * (b1_s * (b2_s * tmp)))

Julia

b2\_m = abs(b2)
b2\_s = copysign(1.0, b2)
b1\_m = abs(b1)
b1\_s = copysign(1.0, b1)
a2\_m = abs(a2)
a2\_s = copysign(1.0, a2)
a1\_m = abs(a1)
a1\_s = copysign(1.0, a1)
a1_m, a2_m, b1_m, b2_m = sort([a1_m, a2_m, b1_m, b2_m])
function code(a1_s, a2_s, b1_s, b2_s, a1_m, a2_m, b1_m, b2_m)
	t_0 = Float64(Float64(a1_m * a2_m) / Float64(b1_m * b2_m))
	t_1 = Float64(a1_m * Float64(a2_m / Float64(b1_m * b2_m)))
	tmp = 0.0
	if (t_0 <= 10.0)
		tmp = t_1;
	elseif (t_0 <= 1e+308)
		tmp = t_0;
	else
		tmp = t_1;
	end
	return Float64(a1_s * Float64(a2_s * Float64(b1_s * Float64(b2_s * tmp))))
end

MATLAB

b2\_m = abs(b2);
b2\_s = sign(b2) * abs(1.0);
b1\_m = abs(b1);
b1\_s = sign(b1) * abs(1.0);
a2\_m = abs(a2);
a2\_s = sign(a2) * abs(1.0);
a1\_m = abs(a1);
a1\_s = sign(a1) * abs(1.0);
a1_m, a2_m, b1_m, b2_m = num2cell(sort([a1_m, a2_m, b1_m, b2_m])){:}
function tmp_2 = code(a1_s, a2_s, b1_s, b2_s, a1_m, a2_m, b1_m, b2_m)
	t_0 = (a1_m * a2_m) / (b1_m * b2_m);
	t_1 = a1_m * (a2_m / (b1_m * b2_m));
	tmp = 0.0;
	if (t_0 <= 10.0)
		tmp = t_1;
	elseif (t_0 <= 1e+308)
		tmp = t_0;
	else
		tmp = t_1;
	end
	tmp_2 = a1_s * (a2_s * (b1_s * (b2_s * tmp)));
end

Wolfram

b2\_m = N[Abs[b2], $MachinePrecision]
b2\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[b2]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
b1\_m = N[Abs[b1], $MachinePrecision]
b1\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[b1]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
a2\_m = N[Abs[a2], $MachinePrecision]
a2\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a2]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
a1\_m = N[Abs[a1], $MachinePrecision]
a1\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a1]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: a1_m, a2_m, b1_m, and b2_m should be sorted in increasing order before calling this function.
code[a1$95$s_, a2$95$s_, b1$95$s_, b2$95$s_, a1$95$m_, a2$95$m_, b1$95$m_, b2$95$m_] := Block[{t$95$0 = N[(N[(a1$95$m * a2$95$m), $MachinePrecision] / N[(b1$95$m * b2$95$m), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(a1$95$m * N[(a2$95$m / N[(b1$95$m * b2$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(a1$95$s * N[(a2$95$s * N[(b1$95$s * N[(b2$95$s * If[LessEqual[t$95$0, 10.0], t$95$1, If[LessEqual[t$95$0, 1e+308], t$95$0, t$95$1]]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 a1 a2) (*.f64 b1 b2)) < 10 or 1e308 < (/.f64 (*.f64 a1 a2) (*.f64 b1 b2))

   1. Initial program 85.3%

      \[\frac{a1 \cdot a2}{b1 \cdot b2} \]
   2. Add Preprocessing

   3. Taylor expanded in a1 around 0

      \[\leadsto \color{blue}{\frac{a1 \cdot a2}{b1 \cdot b2}} \]
   4. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \color{blue}{a1 \cdot \frac{a2}{b1 \cdot b2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \color{blue}{a1 \cdot \frac{a2}{b1 \cdot b2}} \]

      3. lower-/.f64 N/A

         \[\leadsto a1 \cdot \color{blue}{\frac{a2}{b1 \cdot b2}} \]

      4. lower-*.f64 85.8

         \[\leadsto a1 \cdot \frac{a2}{\color{blue}{b1 \cdot b2}} \]

   5. Simplified 85.8%

      \[\leadsto \color{blue}{a1 \cdot \frac{a2}{b1 \cdot b2}} \]

   if 10 < (/.f64 (*.f64 a1 a2) (*.f64 b1 b2)) < 1e308

   1. Initial program 98.7%

      \[\frac{a1 \cdot a2}{b1 \cdot b2} \]
   2. Add Preprocessing
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 86.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} b2\_m = \left|b2\right| \\ b2\_s = \mathsf{copysign}\left(1, b2\right) \\ b1\_m = \left|b1\right| \\ b1\_s = \mathsf{copysign}\left(1, b1\right) \\ a2\_m = \left|a2\right| \\ a2\_s = \mathsf{copysign}\left(1, a2\right) \\ a1\_m = \left|a1\right| \\ a1\_s = \mathsf{copysign}\left(1, a1\right) \\ [a1_m, a2_m, b1_m, b2_m] = \mathsf{sort}([a1_m, a2_m, b1_m, b2_m])\\ \\ a1\_s \cdot \left(a2\_s \cdot \left(b1\_s \cdot \left(b2\_s \cdot \left(a1\_m \cdot \frac{a2\_m}{b1\_m \cdot b2\_m}\right)\right)\right)\right) \end{array} \]

FPCore

b2\_m = (fabs.f64 b2)
b2\_s = (copysign.f64 #s(literal 1 binary64) b2)
b1\_m = (fabs.f64 b1)
b1\_s = (copysign.f64 #s(literal 1 binary64) b1)
a2\_m = (fabs.f64 a2)
a2\_s = (copysign.f64 #s(literal 1 binary64) a2)
a1\_m = (fabs.f64 a1)
a1\_s = (copysign.f64 #s(literal 1 binary64) a1)
NOTE: a1_m, a2_m, b1_m, and b2_m should be sorted in increasing order before calling this function.
(FPCore (a1_s a2_s b1_s b2_s a1_m a2_m b1_m b2_m)
 :precision binary64
 (* a1_s (* a2_s (* b1_s (* b2_s (* a1_m (/ a2_m (* b1_m b2_m))))))))

C

b2\_m = fabs(b2);
b2\_s = copysign(1.0, b2);
b1\_m = fabs(b1);
b1\_s = copysign(1.0, b1);
a2\_m = fabs(a2);
a2\_s = copysign(1.0, a2);
a1\_m = fabs(a1);
a1\_s = copysign(1.0, a1);
assert(a1_m < a2_m && a2_m < b1_m && b1_m < b2_m);
double code(double a1_s, double a2_s, double b1_s, double b2_s, double a1_m, double a2_m, double b1_m, double b2_m) {
	return a1_s * (a2_s * (b1_s * (b2_s * (a1_m * (a2_m / (b1_m * b2_m))))));
}

Fortran

b2\_m = abs(b2)
b2\_s = copysign(1.0d0, b2)
b1\_m = abs(b1)
b1\_s = copysign(1.0d0, b1)
a2\_m = abs(a2)
a2\_s = copysign(1.0d0, a2)
a1\_m = abs(a1)
a1\_s = copysign(1.0d0, a1)
NOTE: a1_m, a2_m, b1_m, and b2_m should be sorted in increasing order before calling this function.
real(8) function code(a1_s, a2_s, b1_s, b2_s, a1_m, a2_m, b1_m, b2_m)
    real(8), intent (in) :: a1_s
    real(8), intent (in) :: a2_s
    real(8), intent (in) :: b1_s
    real(8), intent (in) :: b2_s
    real(8), intent (in) :: a1_m
    real(8), intent (in) :: a2_m
    real(8), intent (in) :: b1_m
    real(8), intent (in) :: b2_m
    code = a1_s * (a2_s * (b1_s * (b2_s * (a1_m * (a2_m / (b1_m * b2_m))))))
end function

Java

b2\_m = Math.abs(b2);
b2\_s = Math.copySign(1.0, b2);
b1\_m = Math.abs(b1);
b1\_s = Math.copySign(1.0, b1);
a2\_m = Math.abs(a2);
a2\_s = Math.copySign(1.0, a2);
a1\_m = Math.abs(a1);
a1\_s = Math.copySign(1.0, a1);
assert a1_m < a2_m && a2_m < b1_m && b1_m < b2_m;
public static double code(double a1_s, double a2_s, double b1_s, double b2_s, double a1_m, double a2_m, double b1_m, double b2_m) {
	return a1_s * (a2_s * (b1_s * (b2_s * (a1_m * (a2_m / (b1_m * b2_m))))));
}

Python

b2\_m = math.fabs(b2)
b2\_s = math.copysign(1.0, b2)
b1\_m = math.fabs(b1)
b1\_s = math.copysign(1.0, b1)
a2\_m = math.fabs(a2)
a2\_s = math.copysign(1.0, a2)
a1\_m = math.fabs(a1)
a1\_s = math.copysign(1.0, a1)
[a1_m, a2_m, b1_m, b2_m] = sort([a1_m, a2_m, b1_m, b2_m])
def code(a1_s, a2_s, b1_s, b2_s, a1_m, a2_m, b1_m, b2_m):
	return a1_s * (a2_s * (b1_s * (b2_s * (a1_m * (a2_m / (b1_m * b2_m))))))

Julia

b2\_m = abs(b2)
b2\_s = copysign(1.0, b2)
b1\_m = abs(b1)
b1\_s = copysign(1.0, b1)
a2\_m = abs(a2)
a2\_s = copysign(1.0, a2)
a1\_m = abs(a1)
a1\_s = copysign(1.0, a1)
a1_m, a2_m, b1_m, b2_m = sort([a1_m, a2_m, b1_m, b2_m])
function code(a1_s, a2_s, b1_s, b2_s, a1_m, a2_m, b1_m, b2_m)
	return Float64(a1_s * Float64(a2_s * Float64(b1_s * Float64(b2_s * Float64(a1_m * Float64(a2_m / Float64(b1_m * b2_m)))))))
end

MATLAB

b2\_m = abs(b2);
b2\_s = sign(b2) * abs(1.0);
b1\_m = abs(b1);
b1\_s = sign(b1) * abs(1.0);
a2\_m = abs(a2);
a2\_s = sign(a2) * abs(1.0);
a1\_m = abs(a1);
a1\_s = sign(a1) * abs(1.0);
a1_m, a2_m, b1_m, b2_m = num2cell(sort([a1_m, a2_m, b1_m, b2_m])){:}
function tmp = code(a1_s, a2_s, b1_s, b2_s, a1_m, a2_m, b1_m, b2_m)
	tmp = a1_s * (a2_s * (b1_s * (b2_s * (a1_m * (a2_m / (b1_m * b2_m))))));
end

Wolfram

b2\_m = N[Abs[b2], $MachinePrecision]
b2\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[b2]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
b1\_m = N[Abs[b1], $MachinePrecision]
b1\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[b1]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
a2\_m = N[Abs[a2], $MachinePrecision]
a2\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a2]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
a1\_m = N[Abs[a1], $MachinePrecision]
a1\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a1]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: a1_m, a2_m, b1_m, and b2_m should be sorted in increasing order before calling this function.
code[a1$95$s_, a2$95$s_, b1$95$s_, b2$95$s_, a1$95$m_, a2$95$m_, b1$95$m_, b2$95$m_] := N[(a1$95$s * N[(a2$95$s * N[(b1$95$s * N[(b2$95$s * N[(a1$95$m * N[(a2$95$m / N[(b1$95$m * b2$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 87.0%

   \[\frac{a1 \cdot a2}{b1 \cdot b2} \]
2. Add Preprocessing

3. Taylor expanded in a1 around 0

   \[\leadsto \color{blue}{\frac{a1 \cdot a2}{b1 \cdot b2}} \]
4. Step-by-step derivation

   1. associate-/l* N/A

      \[\leadsto \color{blue}{a1 \cdot \frac{a2}{b1 \cdot b2}} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{a1 \cdot \frac{a2}{b1 \cdot b2}} \]

   3. lower-/.f64 N/A

      \[\leadsto a1 \cdot \color{blue}{\frac{a2}{b1 \cdot b2}} \]

   4. lower-*.f64 85.6

      \[\leadsto a1 \cdot \frac{a2}{\color{blue}{b1 \cdot b2}} \]

5. Simplified 85.6%

   \[\leadsto \color{blue}{a1 \cdot \frac{a2}{b1 \cdot b2}} \]
6. Add Preprocessing

Developer Target 1: 85.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \frac{a1}{b1} \cdot \frac{a2}{b2} \end{array} \]

FPCore

(FPCore (a1 a2 b1 b2) :precision binary64 (* (/ a1 b1) (/ a2 b2)))

C

double code(double a1, double a2, double b1, double b2) {
	return (a1 / b1) * (a2 / b2);
}

Fortran

real(8) function code(a1, a2, b1, b2)
    real(8), intent (in) :: a1
    real(8), intent (in) :: a2
    real(8), intent (in) :: b1
    real(8), intent (in) :: b2
    code = (a1 / b1) * (a2 / b2)
end function

Java

public static double code(double a1, double a2, double b1, double b2) {
	return (a1 / b1) * (a2 / b2);
}

Python

def code(a1, a2, b1, b2):
	return (a1 / b1) * (a2 / b2)

Julia

function code(a1, a2, b1, b2)
	return Float64(Float64(a1 / b1) * Float64(a2 / b2))
end

MATLAB

function tmp = code(a1, a2, b1, b2)
	tmp = (a1 / b1) * (a2 / b2);
end

Wolfram

code[a1_, a2_, b1_, b2_] := N[(N[(a1 / b1), $MachinePrecision] * N[(a2 / b2), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024215 
(FPCore (a1 a2 b1 b2)
  :name "Quotient of products"
  :precision binary64

  :alt
  (! :herbie-platform default (* (/ a1 b1) (/ a2 b2)))

  (/ (* a1 a2) (* b1 b2)))