Paper Info
Title
Performance Optimization of Top-k Queries on GPU.
Abstract
With the development of web search engines, the concern on real-time performance of Top-k queries has attracted more and more attention. The author studies implement of classic algorithm No Random Access Algorithm in order to optimize performance of Top-k queries on GPU. We give a novel GPU algorithm by using the features of CUDA's programming model. Experiment results show that an implementation of the algorithm on one GPU runs more than 7000 times faster than a single core implementation on a latest CPU.
Year
DOI
Venue
2011
10.1109/PAAP.2011.11
PAAP
Keywords
Field
DocType
novel gpu algorithm,latest cpu,experiment result,top-k queries,classic algorithm,performance optimization,author study,top-k query,real-time performance,programming model,random access algorithm,single core implementation,optimization,upper bound,random access,web search engine,programming,parallel programming,vectors,real time,algorithm design and analysis,instruction sets,algorithm design
Single-core,Search engine,Algorithm design,Programming paradigm,Instruction set,CUDA,Computer science,Parallel computing,Graphics processing unit,Random access
Conference
Citations 
PageRank 
References 
0
0.34
8
Authors
3
Name
Order
Citations
PageRank
Tao Luo1114.87
Guangzhong Sun2131279.74
Guoliang Chen330546.48