ParaTreeT: A Fast, General Framework for Spatial Tree Traversal

Tree-based algorithms for spatial domain applications scale poorly in the distributed setting without extensive experimentation and optimization. Reusability via well-designed parallel abstractions supported by efficient parallel algorithms is therefore desirable. We present ParaTreeT, a parallel tree toolkit for state-of-the-art performance and programmer productivity. ParaTreeT leverages a novel shared-memory software cache to reduce communication volume and idle time throughout traversal. By dividing particles and subtrees across processors independently, it improves decomposition and limits synchro-nization during tree build. Tree-node states are extracted from the particle set with the Data abstraction, and traversal work and pruning are defined by the Visitor abstraction. ParaTreeT provides built-in trees, decompositions, and traversals that offer application-specific customization. We demonstrate ParaTreeT's improved computational performance over even specialized codes with multiple applications on CPUs. We evaluate how several applications derive benefit from ParaTreeT's models while pro-viding new insights to these workloads through experimentation.