Massively parallel model of evolutionary game dynamics
To study the emergence of cooperative behavior, we have developed a scalable parallel framework. An important aspect is the amount of history that each agent can keep. When six memory steps are taken into account, the strategy space spans 24096 potential strategies, requiring large populations of agents. We introduce a multi-level decomposition method that allows us to exploit both multi-node and thread-level parallel scaling while minimizing the communication overhead. We present the following contributions: (1) A production run modeling up to six memory steps for populations consisting of up to 1018 agents, making this study one of the largest yet undertaken. (2) Results exhibiting near perfect weak scaling and 82% strong scaling efficiency up to 262,144 processors of the IBM Blue Gene/P supercomputer and 16,384 processors of the Blue Gene/Q. Our framework marks an important step in the study of game dynamics with potential applications in fields ranging from biology to economics and sociology. © 2012 IEEE.
