Average Error: 0.0 → 0.0
Time: 1.8s
Precision: binary64
\[\left(\frac{x}{2} + y \cdot x\right) + z \]
↓
\[\mathsf{fma}\left(x, 0.5, \mathsf{fma}\left(x, y, z\right)\right) \]
(FPCore (x y z) :precision binary64 (+ (+ (/ x 2.0) (* y x)) z))
↓
(FPCore (x y z) :precision binary64 (fma x 0.5 (fma x y z)))
double code(double x, double y, double z) {
	return ((x / 2.0) + (y * x)) + z;
}
↓
double code(double x, double y, double z) {
	return fma(x, 0.5, fma(x, y, z));
}

function code(x, y, z)
	return Float64(Float64(Float64(x / 2.0) + Float64(y * x)) + z)
end
↓
function code(x, y, z)
	return fma(x, 0.5, fma(x, y, z))
end

code[x_, y_, z_] := N[(N[(N[(x / 2.0), $MachinePrecision] + N[(y * x), $MachinePrecision]), $MachinePrecision] + z), $MachinePrecision]
↓
code[x_, y_, z_] := N[(x * 0.5 + N[(x * y + z), $MachinePrecision]), $MachinePrecision]

\left(\frac{x}{2} + y \cdot x\right) + z
↓
\mathsf{fma}\left(x, 0.5, \mathsf{fma}\left(x, y, z\right)\right)

Error

Bits error versus x

Bits error versus y

Bits error versus z

Derivation

  1. Initial program 0.0

    \[\left(\frac{x}{2} + y \cdot x\right) + z \]

  2. Simplified0.0

    \[\leadsto \color{blue}{\frac{x}{2} + \mathsf{fma}\left(x, y, z\right)} \]

  3. Applied egg-rr0.0

    \[\leadsto \color{blue}{\mathsf{fma}\left(x, 0.5, \mathsf{fma}\left(x, y, z\right)\right)} \]

  4. Final simplification0.0

    \[\leadsto \mathsf{fma}\left(x, 0.5, \mathsf{fma}\left(x, y, z\right)\right) \]

Reproduce

herbie shell --seed 2022160 
(FPCore (x y z)
  :name "Data.Histogram.Bin.BinF:$cfromIndex from histogram-fill-0.8.4.1"
  :precision binary64
  (+ (+ (/ x 2.0) (* y x)) z))