The allocation of the computational load across different processing elements is an important issue in parallel computing. Indeed, an unbalanced load distribution can strongly affect the performances of a parallel system caused by an excess of synchronization idle times due to less loaded processes waiting for more loaded ones. In this article, we focus on the load balancing issues in the context of the parallel execution of spatial-related applications where the domain space is partitioned in regions that are assigned to different processing elements. In particular, without loss of generality, we consider the well-known spatial-related Cellular Automata computational paradigm for evaluating the proposed dynamic load balancing approach. The main contribution of this article is the derivation of simple closed-form expressions that allow to compute the optimal workload assignment in a dynamic fashion, with the goal of guaranteeing a fully balanced workload distribution during the parallel execution. Based on these expressions, an algorithm for balanced execution of cellular automata is presented and implemented using the MPI technology. Eventually, an experimental section practically shows the behaviour of the proposed dynamic load balancing approach and proves its performance improvement, compared to the not-balanced version, as witnessed by the appreciable reduction of execution times.

中文翻译：

---

元胞自动机并行执行中的动态负载平衡


不同处理单元之间的计算负载分配是并行计算中的一个重要问题。事实上，不平衡的负载分布会严重影响并行系统的性能，这是由于负载较少的进程等待负载较多的进程而导致同步空闲时间过多造成的。在本文中，我们重点关注空间相关应用程序并行执行背景下的负载平衡问题，其中域空间被划分为分配给不同处理元素的区域。特别是，在不失一般性的情况下，我们考虑众所周知的空间相关元胞自动机计算范例来评估所提出的动态负载平衡方法。本文的主要贡献是推导了简单的封闭式表达式，允许以动态方式计算最佳工作负载分配，目标是保证并行执行期间完全平衡的工作负载分配。基于这些表达式，提出了一种元胞自动机平衡执行算法，并使用 MPI 技术实现。最终，实验部分实际展示了所提出的动态负载平衡方法的行为，并证明了与非平衡版本相比其性能的改进，执行时间的显着减少就证明了这一点。