Parallel Execution of kNN-Queries on in-memory K-D Trees

Abstract: Parallel algorithms for main memory databases become an increasingly interesting topic as the amount of main memory and the number of CPU cores in computer systems increase. This paper suggests a method for parallelizing the k-d tree and its kNN search algorithm as well as suggesting optimizations. In empirical tests, the resulting modified k-d tree outperforms both the k-d tree and a parallelized sequential search for medium dimensionality data (6-13 dimensions).