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

K最近邻居（KNN）联接在科学领域中用于数据分析，并且是几种众所周知的算法的构建块。KNN -joins查找数据集中所有点的KNN。本文重点介绍用于低维KNN联接的混合CPU / GPU方法，其中GPU可能不会比并行CPU算法产生实质性的性能提升。我们利用工作队列来优先处理GPU上高密度区域和CPU上低密度区域的计算数据点，从而利用每种架构的相对优势。我们的方法HybridKNN-Join有效地增强了最新的多核CPU算法。我们建议优化$（\mathrm{一世}）$ 通过分配GPU大量工作来最大化GPU查询吞吐量； $（\mathrm{一世}\mathrm{一世}）$增加工作负载粒度以优化GPU利用率；和，$（\mathrm{一世}\mathrm{一世}\mathrm{一世}）$限制CPU和GPU架构之间的负载不平衡。我们将HybridKNN-Join与一个GPU和两个并行CPU参考实现进行比较。与参考实现相比，我们发现混合算法在较大的工作负载（数据集大小和K）上表现最佳。本文采用的方法为其他混合算法中的一般工作划分显示了希望。