Simulating Population Protocols in Sub-Constant Time per Interaction [article]

Petra Berenbrink and David Hammer and Dominik Kaaser and Ulrich Meyer and Manuel Penschuck and Hung Tran
2020 arXiv   pre-print
We consider the problem of efficiently simulating population protocols. In the population model, we are given a distributed system of n agents modeled as identical finite-state machines. In each time step, a pair of agents is selected uniformly at random to interact. In an interaction, agents update their states according to a common transition function. We empirically and analytically analyze two classes of simulators for this model. First, we consider sequential simulators executing one
more » ... ction after the other. Key to the performance of these simulators is the data structure storing the agents' states. For our analysis, we consider plain arrays, binary search trees, and a novel Dynamic Alias Table data structure. Secondly, we consider batch processing to efficiently update the states of multiple independent agents in one step. For many protocols considered in literature, our simulator requires amortized sub-constant time per interaction and is fast in practice: given a fixed time budget, the implementation of our batched simulator is able to simulate population protocols several orders of magnitude larger compared to the sequential competitors, and can carry out 2^50 interactions among the same number of agents in less than 400s.
arXiv:2005.03584v1 fatcat:dwxl5y53lrcrznwpkvxf6cdmpy