Key points are not available for this paper at this time.
The Monte Carlo (MC) method is an important tool in sampling the state space of a chosen statistical ensemble. It allows the study of thermodynamic averages of configurational properties by generating ``moves'' in a system and accepting or rejecting the thus generated new state depending on the energy of the new system and/or a random choice. These moves are intrinsically sequential and complicate parallel implementation. We propose a method which allows the parallel generation of MC moves, and which is especially useful for simulations with unavoidably low acceptance rates, such as for long chain molecules.
Esselink et al. (1995) studied this question.