SPAWN: An Iterative, Potentials-Based, Dynamic Scheduling and Partitioning Tool

Abstract

Many applications of physics modeling use regular meshes on which computations of highly variable cost over time can occur. Distributing the underlying cells over manycore architectures is a critical load balancing step that should be performed the less frequently possible. 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 increase. We introduce a dynamic task scheduling and mesh partitioning approach inspired by physical particle interactions. Our method virtually moves cores over a 2D/3D mesh of tasks and uses a Voronoi domain decomposition to balance workload. 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.