A Preliminary Analysis on BLMAB-Based Approximate Optimization Support for Multi Agent Simulations on Multi-core and GPU-Based Computing Environment

There are strong demands to utilize multi-core computing resources effectively for large-scale and highly detailed multi-agent simulations. We have proposed a framework to assist parameter tuning process of multi-core programming for simulation developers to utilize many parallel cores in their simulation programs efficiently. However, due to its massive computation costs, it is not easy task to seek the sufficient compilation and execution parameters and analyze their performance characteristics for various execution settings. In this paper, we present a preliminary analysis of parameter optimization based on BLMAB by utilizing our framework. We show how our BLMAB-based approach can effectively be used on the parameter optimization process.