Accelerating Large Scale Artificial Society Simulation with CPU/GPU Based Heterogeneous Parallel Method

摘要

Artificial society is an effective way for social science research. However, in order to meet real-time and super real-time requirement of computational experiment, the execution efficiency of large-scale artificial society then becomes the burning question. The emergence of heterogeneous parallel system offers opportunities and challenges for accelerating large scale artificial society simulation. How to fully utilize heterogeneous computational resources in large scale agent based simulation becomes the key issue. The paper proposes a CPU/GPU-based accelerating computational method, in which GPU is fully utilized in two different ways at the same time. Firstly GPU is treated as host processor, and a GPU based simulation kernel is designed to execute the models collaboratively with CPU simulation kernel. Secondly, in order to accelerate the domain-specific models, a specific domain-oriented GPU simulation computational service component is proposed, and GPU is used as a co-processor to offer domain-specific parallel optimization. A SPMT (Single Process Multi Threads) based conservative parallel simulation framework is proposed to integrate the GPU simulation kernel and computational service component. At last, an experiment is designed to test the efficiency of GPU based simulation kernel, and argues about the application mode of GPU.