On Implementing a Parallel Integer Solver Using Optimization Services
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Transcript of On Implementing a Parallel Integer Solver Using Optimization Services
Jun Ma, Sanjay Mehrotra and Huanyuan ShengImpact Solver for Optimization Services, November 8, 2006
On Implementing a Parallel Integer Solver
Using Optimization Services
Jun Ma
Huanyuan (Wayne) Sheng
Joint work with
Sanjay Mehrotra
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Jun Ma, Sanjay Mehrotra and Huanyuan ShengImpact Solver for Optimization Services, November 8, 2006
Outline
• Brief Introduction– Impact Generalized MIP Solver – Optimization Services (OS)
• Distributed Parallel For Integer Programming Using OS
• Conclusion
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Jun Ma, Sanjay Mehrotra and Huanyuan ShengImpact Solver for Optimization Services, November 8, 2006
IntroductionImpact GMIP Solver
• IMPACT -- Integrated Mathematical Programming Advanced Computational Tools
• Features– Generalized Mixed Integer Nonlinear Solver (GMIP).– Generalized Hyperplanes based Branch and Bound.– Standalone Solver and Remote Solver Service.– Unified NATIVE Interface with Optimization Services.
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Jun Ma, Sanjay Mehrotra and Huanyuan ShengImpact Solver for Optimization Services, November 8, 2006
Impact GMIP Solver Features Algorithms
• Research Focus– Mixed Integer Nonlinear Programming– Parallel computing for MINLP
• Algorithm Studies– Heuristics for generalized branch and bound
methods – Optimization Services based distributed parallel,
e.g. communications, load balance handling.
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Jun Ma, Sanjay Mehrotra and Huanyuan ShengImpact Solver for Optimization Services, November 8, 2006
Impact GMIP Solver Features Algorithms
RootNode
Proper BranchingHyperplane
being u
Leftchild
RightChild
Add u
lT xu 1 l
T xu
Growing Left Growing Right
Generate Children Generate Children
• Starting Node
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Jun Ma, Sanjay Mehrotra and Huanyuan ShengImpact Solver for Optimization Services, November 8, 2006
Impact GMIP Solver Features Algorithms
• Subsequent Node
ParentNode(Left)
Proper BranchingHyperplane
being u prime
Leftchild
RightChild
Add new u prime
lT xu ''
Growing Left Growing Right
Generate Children Generate Children
SiblingNode
1 lT xu
1'' lT xu
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Jun Ma, Sanjay Mehrotra and Huanyuan ShengImpact Solver for Optimization Services, November 8, 2006
Impact GMIP Solver Features Algorithms
• Generate Proper Branching Hyperplanes
– Basis Reduction Based (Mehrotra and Li)• LLL• GBR (Generalized Basis Reduction)
– Heuristics (ongoing)
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Jun Ma, Sanjay Mehrotra and Huanyuan ShengImpact Solver for Optimization Services, November 8, 2006
GenerateNodes
Instance
Number OfNodes > 100
N
NodeQueue
Y
DistributedSystem
DistributedSystem
DistributedSystem
End
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Jun Ma, Sanjay Mehrotra and Huanyuan ShengImpact Solver for Optimization Services, November 8, 2006
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Jun Ma, Sanjay Mehrotra and Huanyuan ShengImpact Solver for Optimization Services, November 8, 2006
IMPACT GMIP Parallelization over Distributed Optimization Services
ImpactGMIP
OShL
OShL
OShL
OShL
OShL - hookup
CommunicationgetJobID (String OSoL)
solve (String OSiL, String OSoL)
send (String OSiL, String OSoL)
retrieve (String OSoL)
kill (String OSoL)
knock (String OSpL, String OSoL)
OSiL - instance
OSoL - option
Representation
OSrL - result
OSpL - process
Nodes (OSiL Integer)
OSServer
(Linux)
OSServer
(WinXP)
OSServer
(Mac OS)
Lindo
CPLEX
IMPACT
Call back
(OSrL)
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Jun Ma, Sanjay Mehrotra and Huanyuan ShengImpact Solver for Optimization Services, November 8, 2006
Conclusion
• Introduced the Generalized MIP and showed it is friendliness for Parallelization
• Showed Optimization Services has a general and high extendable design fit for many derived researches
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Jun Ma, Sanjay Mehrotra and Huanyuan ShengImpact Solver for Optimization Services, November 8, 2006