Parallel Skyline Computation on Multicore Architectures

ICDE`09

OutlineIntroductionPreliminaryParallel BBS(branch-and-bound

algorithm)Parallel Skyline Algorithm

(Pskyline)ExperimentsConclusion

Introduction

(cont.)The advantage of the skylineSkyline algorithm

◦Sequential No index structure

BNL、 SFS 、 LESS Index structure

NN 、 BBS 、 ZSHARE

◦Parallel Many in Distributed environment, but no

algorithm for multi-core environment

PrrliminaryA. Skyline computation

◦For a d-dimensional dataset D. skyline set : Incomparable : Transitivity : Incomparability :

Distributivity :

(cont.)B.Skeletal parallel programming

◦A programming model Need two component :

Data structure List :

Parallel skeletons not

For developing parallel skyline algorithm Parallel map and Parallel reduce

(cont.)Pmap (parallel map)

◦

A B C D E F G H

A’ B’ C’ D’ E’ F’ G’ H’

(cont.)Preduce (parallel reduce)

◦

A B C D E F G H

A’ B’ C’ D’

R

……

Parallel BBSA. branch-and-bound algorithm

◦Use R-tree as its index structure

(cont.)B.Parallelizing BBS

Parallel skyline algorithm (Pskyline)Overall design

◦Pskyline D = S(D)◦Pskyline D = sreduce pmerge (pmap

sskyline L) L = [D1,…,Db] D = D1++…++Db

Sreduce

(cont.)Pmerge

Sskyline

Experiments

(cont.)

ConclusionMuliti-core architecture for

database operations.