MPR — A Partitioning-Replication Framework for Multi-Processing kNN Search on Road Networks

Abstract

We study the problem of executing road-network k-nearest-neighbor (kNN) search on multi-core machines. State-of-the-art kNN algorithms on road networks often involve elaborate index structures and complex computational logic. Moreover, most kNN algorithms are inherently sequential. These make the traditional approach of parallel programming very costly, laborious, and ineffective when they are applied to kNN algorithms. We propose the MPR (Multi-layer Partitioning-Replication) mechanism that orchestrates CPU cores and schedules kNN query and index update processes to run on the cores. The MPR mechanism performs workload analysis to determine the best arrangement of the cores with the objective of optimizing quality-of-service (QoS) measures, such as system throughput and query response time. We demonstrate the effectiveness of MPR by applying it to a number of state-of-the-art kNN indexing methods running on a multi-core machine. Our experiments show that multi-processing using our MPR approach requires minimal programming effort. It also leads to significant improvements in query response time and system throughput compared with other baseline parallelization methods