Spatial simulations of crop growth under climate change have been limited to researchers who have access to the resources for high performance computing. The objective of this study was to develop an orchestration aid system for concurrent gridded simulations of crop growth, which would support the design of climate change adaptation options on crop production without expertise in distributed computing. The orchestration aid system was designed to help a user build a set of virtualized cluster computers using a simple input file, which would require little expertise in distributed computing, rather than manual configuration. The orchestration aid system, which was referred to as GROWLERS-kube, was implemented to launch multiple sets of gridded simulations using pods or containers managed by Kubernetes. As a case study, GROWLER-kube was executed using 16 Raspberry Pi 4 computers to perform 120 sets of the gridded simulations under diverse crop management options, including varying planting date and cultivar, for the period from 2001 to 2010. The wall time or the elapsed time for the given sets of the gridded simulation differed by configuration of virtualized cluster computers, such as the number of pods used for server and client nodes, although the total number of physical nodes were identical. For example, the wall time difference between virtualized cluster computer sets was about 28.9% when 15 worker nodes were used. In particular, the acceleration of the gridded simulations was at maximum using a large number of the virtualized cluster computers with a small number of nodes. It was found that the spatial distribution of planting dates and cultivars was similar to that of a previous study based on field experiments mostly in regions where rice is usually grown. These results suggest that GROWLERS-kube would facilitate the spatial assessment of climate change impact on crop production without considerable effort and expertise in distributed computing, which would aid a researcher to focus on the design of adaptation strategies.

气候变化下作物生长的空间模拟仅限于能够获得高性能计算资源的研究人员。这项研究的目的是开发一个编排辅助系统，用于同时进行作物生长的网格模拟，这将支持在没有分布式计算专业知识的情况下针对作物生产设计适应气候变化的方案。编排辅助系统旨在帮助用户使用简单的输入文件来构建一组虚拟化的群集计算机，该文件只需要很少的分布式计算专业知识，而无需人工配置。编排辅助系统（称为GROWLERS-kube）被实现为使用Kubernetes管理的容器或容器启动多组网格化模拟。作为案例研究 使用16台Raspberry Pi 4计算机执行GROWLER-kube，在2001年至2010年期间，在不同的作物管理选项下（包括不同的播种日期和品种）执行120套网格模拟。网格化模拟的集合因虚拟化群集计算机的配置（例如，用于服务器和客户端节点的Pod的数量）而有所不同，尽管物理节点的总数是相同的。例如，当使用15个工作节点时，虚拟化群集计算机集之间的隔离时间差约为28.9％。特别是，使用大量带有少量节点的虚拟化群集计算机时，网格化仿真的加速最大。结果发现，播种日期和品种的空间分布与以前基于田间试验的研究相似，主要是在通常种植水稻的地区进行的。这些结果表明，GROWLERS-kube无需在分布式计算上花费大量精力和专业知识，就可以促进气候变化对作物生产影响的空间评估，这将有助于研究人员专注于适应策略的设计。