.png){kind=icon}
返回
A Hybrid Approach for Optimizing Parallel Clustering Throughput using the GPU
.png){kind=icon}
OA
AI
DOI：10.1109/TPDS.2018.2869777.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 epsilon search distances.
Keyword:
DBSCAN
GPGPU
in-memory database
parallel clustering
query optimization
spatial databases
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
5.2K
被引数:
1.1W
机构
引用论文
.png){kind=icon}
Copolymerization of pairs of olefines with sulphur dioxide in the region of the ceiling temperature
PREAI
分享收藏
查看更多内容