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Parallel Skyline Computation on Multicore ArchitecturesPowerPoint Presentation

Parallel Skyline Computation on Multicore Architectures

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Parallel Skyline Computation on Multicore Architectures

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Parallel Skyline Computation on Multicore Architectures

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Parallel Skyline Computation on Multicore Architectures

ICDE`09

- Introduction
- Preliminary
- Parallel BBS(branch-and-bound algorithm)
- Parallel Skyline Algorithm (Pskyline)
- Experiments
- Conclusion

- The advantage of the skyline
- Skyline algorithm
- Sequential
- No index structure
- BNL、SFS 、LESS

- Index structure
- NN 、BBS 、ZSHARE

- No index structure
- Parallel
- Many in Distributed environment, but no algorithm for multi-core environment

- Sequential

- A. Skyline computation
- For a d-dimensional dataset D.
- skyline set :
- Incomparable :
- Transitivity :
- Incomparability :
- Distributivity :

- For a d-dimensional dataset D.

- 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

- Data structure

- Need two component :

- A programming model

- Pmap (parallel map)

A

B

C

D

E

F

G

H

A’

B’

C’

D’

E’

F’

G’

H’

- Preduce (parallel reduce)

A

B

C

D

E

F

G

H

A’

B’

C’

D’

……

R

- A. branch-and-bound algorithm
- Use R-tree as its index structure

- B.Parallelizing BBS

- Overall design
- Pskyline D = S(D)
- PskylineD = sreducepmerge (pmapsskyline L)
- L = [D1,…,Db] D = D1++…++Db

- Sreduce

- Pmerge
- Sskyline

- Muliti-core architecture for database operations.