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ParaTreeT: A Fast, General Framework for Spatial Tree Traversal
| Joseph Hutter | Justin Szaday | Jaemin Choi | Spencer Wallace | Simeng Liu | Laxmikant Kale | Thomas Quinn
IEEE International Parallel and Distributed Processing Symposium (IPDPS) 2022
Publication Type: Paper
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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 synchronization 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 providing new insights to these workloads through experimentation.
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