A Lossy, Synchronization-Free, Race-Full, But Still Acceptably Accurate Parallel Space-Subdivision Tree Construction Algorithm
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We present a new synchronization-free space-subdivision tree construction algorithm. Despite data races, this algorithm produces trees that are consistent enough for the client Barnes-Hut center of mass and force computation phases to use successfully. Our performance results show that eliminating synchronization improves the performance of the parallel algorithm by approximately 20%. End-to-end accuracy results show that the resulting partial data structure corruption has a neglible effect on the overall accuracy of the Barnes-Hut N-body simulation. We note that many data structure manipulation algorithms use many of the same basic operations (linked data structure updates and array insertions) as our tree construction algorithm. We therefore anticipate that the basic principles the we develop in this paper may effectively guide future efforts in this area.
Data Structure Repair, Data Races, Acceptability, Parallel Computing, Synchronization