\[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t
\]
↓
\[\mathsf{fma}\left(z \cdot -0.5, y, 0.125 \cdot x\right) + t
\]
(FPCore (x y z t)
:precision binary64
(+ (- (* (/ 1.0 8.0) x) (/ (* y z) 2.0)) t))
↓
(FPCore (x y z t) :precision binary64 (+ (fma (* z -0.5) y (* 0.125 x)) t))
double code(double x, double y, double z, double t) {
return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
}
↓
double code(double x, double y, double z, double t) {
return fma((z * -0.5), y, (0.125 * x)) + t;
}
function code(x, y, z, t)
return Float64(Float64(Float64(Float64(1.0 / 8.0) * x) - Float64(Float64(y * z) / 2.0)) + t)
end
↓
function code(x, y, z, t)
return Float64(fma(Float64(z * -0.5), y, Float64(0.125 * x)) + t)
end
code[x_, y_, z_, t_] := N[(N[(N[(N[(1.0 / 8.0), $MachinePrecision] * x), $MachinePrecision] - N[(N[(y * z), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision]
↓
code[x_, y_, z_, t_] := N[(N[(N[(z * -0.5), $MachinePrecision] * y + N[(0.125 * x), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision]
\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t
↓
\mathsf{fma}\left(z \cdot -0.5, y, 0.125 \cdot x\right) + t