  The Generalized Langevin Equation, in history, arises as a natural fix for
the rather traditional Langevin equation when the random force is no longer
memoryless. It has been proved that with fractional Gaussian noise (fGn) mostly
considered by biologists, the overdamped Generalized Langevin equation
satisfying fluctuation-dissipation theorem can be written as a fractional
stochastic differential equation (FSDE). While the ergodicity is clear for
linear forces, it remains less transparent for nonlinear forces. In this work,
we present both a direct and a fast algorithm respectively to this FSDE model.
The strong orders of convergence are proved for both schemes, where the role of
the memory effects can be clearly observed. We verify the convergence theorems
using linear forces, and then present the ergodicity study of the double well
potentials in both 1D and 2D setups.
