Performance Evaluation of Parallel Computing on Agent-Based Models: The Minority Game Case

Abstract

Simulations of agent-based models developed for topics of learning and inductive reasoning in artificial intelligence, social behavior, decision making, etc., are progressively requiring higher power processes while they increase their participation as management and political decisions support. In this work we develop the implementation of the Minority Game Model for HPC platforms in order to analyze the performance of simulations related to contexts of agent-based models for large scales. We compare times to parallel and sequential processes for several instances and get the corresponding speedup. For this work we use the MPI system with a hardware configuration of Master-Worker (Slave) paradigm with a cluster of upto 10 processors as workers. In order to improve efficiency, we evaluate performances for several sizes of clusters varying the size of the instances of the problem and detect optimum configurations for some instances of simulation.