A Hybrid Approach for Optimizing Parallel Clustering Throughput using the GPU

Michael Gowanlock, Cody M. Rude, David M. Blair, Justin D. Li, Victor Pankratius

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

We introduce Hybrid-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-Dbscan outperforms 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 of Hybrid-Dbscan by utilizing the underlying properties of the datasets. With only a single execution of Hybrid-Dbscan on a dataset, we are able to accurately predict the response time for a range of ϵ ϵ search distances.

Original languageEnglish (US)
Article number8462789
Pages (from-to)766-777
Number of pages12
JournalIEEE Transactions on Parallel and Distributed Systems
Volume30
Issue number4
DOIs
StatePublished - Apr 1 2019

Keywords

  • DBSCAN
  • GPGPU
  • in-memory database
  • parallel clustering
  • query optimization
  • spatial databases

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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