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K Nearest Neighbor (KNN) joins are used in scientific domains for dataanalysis, and are building blocks of several well-known algorithms. KNN-joinsfind the KNN of all points in a dataset. This paper focuses on a hybrid CPU/GPUapproach for low-dimensional KNN-joins, where the GPU may not yield substantialperformance gains over parallel CPU algorithms. We utilize a work queue thatprioritizes computing data points in high density regions on the GPU, and lowdensity regions on the CPU, thereby taking advantage of each architecture'srelative strengths. Our approach, HybridKNN-Join, effectively augments astate-of-the-art multi-core CPU algorithm. We propose optimizations that $(i)$maximize GPU query throughput by assigning the GPU large batches of work;$(ii)$ increase workload granularity to optimize GPU utilization; and, $(iii)$limit load imbalance between CPU and GPU architectures. We compareHybridKNN-Join to one GPU and two parallel CPU reference implementations.Compared to the reference implementations, we find that the hybrid algorithmperforms best on larger workloads (dataset size and K). The methods employed inthis paper show promise for the general division of work in other hybridalgorithms.

Hybrid KNN-Join: Parallel Nearest Neighbor Searches Exploiting CPU and GPU Architectural Features