An efficient clustered adaptive-risk technique for distributed simulation

Abstract

An efficient adaptive approach for parallel and distributed simulation (PADS) is formalized and implemented. The aggressive adaptive-risk (AAR) approach aims at reducing cascading rollbacks in large and complex simulations by clustering optimistic logical processes on each processor, and providing these processes the ability to adjust their degree of risk, at run time, to a good operating point based on observed behavior. The AAR approach is used to develop the Clustered Adaptive Distributed Simulator (CADS), which is implemented on a network of workstations. Details of the CADS implementation are described. Performance results for large synthetic loads are reported and compared to those obtained for the Time Warp optimistic technique.