  In dynamic Monte Carlo simulations, using for example the Metropolis dynamic,
it is often required to simulate for long times and to simulate large systems.
We present an overview of advanced algorithms to simulate for larger times and
to simulate larger systems. The longer-time algorithm focused on is the Monte
Carlo with Absorbing Markov Chains (MCAMC) algorithm. It is applied to
metastability of an Ising model on a small-world network. Simulations of larger
systems often require the use of non-trivial parallelization. Non-trivial
parallelization of dynamic Monte Carlo is shown to allow perfectly scalable
algorithms, and the theoretical efficiency of such algorithms is described.
