We introduceHybrid-Dbscan, that uses the GPU and CPUs for optimizing clustering throughput. The main idea is to exploit the memory bandwidth on the GPU for fast index searches, and optimize data transfers between host and GPU, to alleviate the potential negative performance impact of the PCIe interconnect. We propose and compare two GPU kernels that exploit grid-based indexing schemes to improve neighborhood search performance. We employ a batching scheme for host-GPU data transfers to obviate limited GPU memory, and exploit concurrent operations on the host and GPU. This scheme is robust with respect to both sparse and dense data distributions and avoids buffer overflows that would otherwise degrade performance. We evaluate our approaches on ionospheric total electron content datasets as well as intermediate-redshift galaxies from the Sloan Digital Sky Survey.Hybrid-Dbscanoutperforms the reference implementation across a range of application scenarios, including small workloads, which typically are the domain of CPU-only algorithms. We advance an empirical response time performance model ofHybrid-Dbscanby utilizing the underlying properties of the datasets. With only a single execution ofHybrid-Dbscanon a dataset, we are able to accurately predict the response time for a range of$\epsilon$search distances.

Large datasets in astronomy and geoscience often require clustering and visualizations of phenomena at different densities and scales in order to generate scientific insight. We examine the problem of maximizing clustering throughput for concurrent dataset clustering in spatial dimensions. We introduce a novel hybrid approach that uses GPUs in conjunction with multicore CPUs for algorithmic throughput optimizations. The key idea is to exploit the fast memory on the GPU for index searches and optimize I/O transfers in such a way that the low-bandwidth host-GPU bottleneck does not have a significant negative performance impact. To achieve this, we derive two distinct GPU kernels that exploit grid-based indexing schemes to improve clustering performance. To obviate limited GPU memory and enable large dataset clustering, our method is complemented by an efficient batching scheme for transfers between the host and GPU accelerator. This scheme is robust with respect to both sparse and dense data distributions and intelligently avoids buffer overflows that would otherwise degrade performance, all while minimizing the number of data transfers between the host and GPU. We evaluate our approaches on ionospheric total electron content datasets as well as intermediate-redshift galaxies from the Sloan Digital Sky Survey. Our hybrid approach yields a speedup of up to 50× over the sequential implementation on one of the experimental scenarios, which is respectable for I/O intensive clustering.