Memory management techniques for time warp on a distributed memory machine

This paper examines memory management issues associated with time warp synchronized parallel simulation on distributed memory machines. The paper begins with a summary of the techniques which have been previously proposed for memory management on various parallel processor memory structures. It then concentrates the discussion on parallel simulation executing on a distributed memory computer-a system comprised of separate computers, interconnected by a communications network. An important characteristic of the software developed for such systems is the fact that the dynamic memory is allocated from a pool of memory that is shared by all of the processes at a given processor. This paper presents a new memory management protocol, pruneback, which recovers space by discarding previous states. This is different from all previous schemes such as artificial rollback and cancelback which recover memory space by causing one or more logical processes to roll back to an earlier simulation time. The paper includes an empirical study of a parallel simulation of a closed stochastic queueing network showing the relationship between simulation execution time and amount of memory available. The results indicate that using pruneback is significantly more effective than artificial rollback (adapted for a distributed memory computer) for this problem. In the study, varying the memory limits over a 2:1 range resulted in a 1:2 change in artificial rollback execution time and almost no change in pruneback execution time