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Parallel Skyline Computation on Multicore Architectures. ICDE`09. Outline. Introduction Preliminary Parallel BBS(branch-and-bound algorithm) Parallel Skyline Algorithm ( Pskyline ) Experiments Conclusion. Introduction. (cont.). The advantage of the skyline Skyline algorithm Sequential

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Presentation Transcript
outline
Outline
  • Introduction
  • Preliminary
  • Parallel BBS(branch-and-bound algorithm)
  • Parallel Skyline Algorithm (Pskyline)
  • Experiments
  • Conclusion
slide4
(cont.)
  • The advantage of the skyline
  • Skyline 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
prrliminary
Prrliminary
  • A. Skyline computation
    • For a d-dimensional dataset D.
      • skyline set :
      • Incomparable :
      • Transitivity :
      • Incomparability :
      • Distributivity :
slide6
(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
slide7
(cont.)
  • Pmap (parallel map)

A

B

C

D

E

F

G

H

A’

B’

C’

D’

E’

F’

G’

H’

slide8
(cont.)
  • Preduce (parallel reduce)

A

B

C

D

E

F

G

H

A’

B’

C’

D’

……

R

parallel bbs
Parallel BBS
  • A. branch-and-bound algorithm
    • Use R-tree as its index structure
slide10
(cont.)
  • B.Parallelizing BBS
parallel skyline algorithm pskyline
Parallel skyline algorithm (Pskyline)
  • Overall design
    • Pskyline D = S(D)
    • PskylineD = sreducepmerge (pmapsskyline L)
      • L = [D1,…,Db] D = D1++…++Db
  • Sreduce
slide12
(cont.)
  • Pmerge
  • Sskyline
conclusion
Conclusion
  • Muliti-core architecture for database operations.
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