基于硬件加速器的Agent仿真研究综述
A Survey on Agent-based Simulation using Hardware Accelerators
摘要：
由于单核CPU性能的降低，在高度并行的硬件平台上越来越多地执行计算昂贵的模拟。基于代理的模拟，其中模拟实体的行为具有一定程度的自主性，经常为并行化提供充足的机会。因此，文献中提出的各种方法使用硬件平台（例如众核CPU和GPU、合并的CPU-GPU芯片以及FPGA）表现出相当大的性能增益。通常情况下，一个给定的仿真模型，需要一个技术的组合，以实现高性能，把大量的负担建模。据我们所知，没有系统的概述，基于代理的硬件加速器上的模拟技术已在文献中。为了缩小这一差距，我们提供了一个概述和分类的文献，根据所应用的技术。由于在目前的研究状态，挑战，如异构硬件上执行的模型的分区仍然是一个很大程度上手动的过程中，我们勾画了未来的研究方向自动化的硬件映射和执行。本次调查的目标是为特定仿真模型寻求合适的硬件平台和执行技术概述的建模人员，以及对需要进一步探索的潜在研究差距感兴趣的方法研究人员。
英文摘要：
Due to decelerating gains in single-core CPU performance, computationally expensive simulations are increasingly executed on highly parallel hardware platforms. Agent-based simulations, where simulated entities act with a certain degree of autonomy, frequently provide ample opportunities for parallelisation. Thus, a vast variety of approaches proposed in the literature demonstrated considerable performance gains using hardware platforms such as many-core CPUs and GPUs, merged CPU-GPU chips as well as FPGAs. Typically, a combination of techniques is required to achieve high performance for a given simulation model, putting substantial burden on modellers. To the best of our knowledge, no systematic overview of techniques for agent-based simulations on hardware accelerators has been given in the literature. To close this gap, we provide an overview and categorisation of the literature according to the applied techniques. Since at the current state of research, challenges such as the partitioning of a model for execution on heterogeneous hardware are still a largely manual process, we sketch directions for future research towards automating the hardware mapping and execution. This survey targets modellers seeking an overview of suitable hardware platforms and execution techniques for a specific simulation model, as well as methodology researchers interested in potential research gaps requiring further exploration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
