Many applications of physics modeling use regular meshes on which computations of highly variable cost can occur. Distributing the underlying cells over manycore architec-tures is a critical load balancing step that should increase the period until another step is required. Graph partitioning tools are known to be very effective for such problems, but they exhibit scalability problems as the number of cores and the number of cells increases. We introduce a dynamic task scheduling approach inspired by physical particles interactions. Our method allows cores to virtually move over a 2D/3D mesh of tasks and uses a Voronoi domain decomposition to balance workload among cores. Displacements of cores are the result of force computations using a carefully chosen pair potential. We evaluate our method against graph partitioning tools and existing task schedulers with a representative physical application, and demonstrate the relevance of our approach.

中文翻译：

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SPAWN：一种迭代的、基于潜力的、动态调度和分区工具

物理建模的许多应用使用规则网格，在这些网格上可以进行高度可变的成本计算。在多核架构上分布底层单元是一个关键的负载平衡步骤，它应该增加周期，直到需要另一个步骤。众所周知，图分区工具对此类问题非常有效，但随着内核数量和单元数量的增加，它们会出现可扩展性问题。我们引入了一种受物理粒子相互作用启发的动态任务调度方法。我们的方法允许内核在任务的 2D/3D 网格上虚拟移动，并使用 Voronoi 域分解来平衡内核之间的工作负载。核心的位移是使用精心选择的对势进行力计算的结果。