The Workflow Framework for Metaheuristic Search on

Grid EnvironmentDang-Khoa Tran

Van Hoai TranFaculty of Computer Science and Engineering

HCMC University of Technology

HPSC 2009 2

Combinatorial optimization problems are often NP-hard, cannot solve in reasonable time.

Meta-heuristic is not like the exact methods, it can find reasonable solutions in acceptable time.

Metaheuristic

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Improving the solutions by expanding the exploration space and with various strategies◦ Take more time◦ … or required more computing resources

Our approach: using Grid Computing to solve the metaheuristic problem.

Metaheuristic

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Meta-heuristic and Grid Computing Design goals The framework designs Experiments and results

Content

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Grid Computing is the parallel and distributed system that enables the sharing and using resources easily.

Grid programming paradigm: using Parallel Object C++ (POP-C++) for application development.◦ Similar to C++ with a few keyword.◦ The objects are automatically created and

executed on Grid.

Grid computing

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User is usually not familiar with Grid programming model.

Application testing and debugging on Grid is difficult.

Make use of Grid computing by executing many search algorithm concurrently.

Some challenges

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Provide workflow model to solve the problems

Easy to develop applications◦ Using OO programming◦ Hide the Grid programming model from user

Test and debug the applications Deploy and execute on Grid environment

easily Running efficiency

Design Goals for the Framework

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Parallel Metaheuristic Library

Library Architecture

Search Algorithms

Workflow Engine

POP-C++ Runtime

Computing Resources(Workstations / Clusters / Grid)

Applications

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Using the search algorithms◦ Inherits from predefined interfaces◦ Implements the abstract methods

Using workflow◦ Create dependency graph◦ The workflow engine will handles the execution

Easy to Use+fullEvaluate() : double+Serialize() : void+printOn() : void+operator =()+operator ==() : bool

edaSolution

mySolution

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// Define search algorithm myTwoOpt two_opt(graph); myTwoOptNext two_opt_next(graph); edaBestImprSelect moveSelect; edaHC hcSearch( &two_opt, &two_opt_next, &moveSelect); /* Define more search algorithms... */ // Define workflow control: Using Parallel workflow edaParWrapperControl sfControl; // Create dependency graph int hcId = sfControl.insertVertex( &hcSearch ); sfControl.insertEdge( hcId, saId ); // Do search sfControl.search(route)

Easy to Use – Example

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Sample workflow

HC

TS

SA

SA TS

SA

HCS

Initial solution

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Easy to Deploy

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Testing and Debugging◦ Using the sequential workflow◦ Test and debug like normal applications

Easy to Test and Debug

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The Traveling Salesman Problem

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The Traveling Salesman Problem Workflow design for testing

HC

SA

TS

HC

HC

SA

TS

HC

HC...HC

Input: graph Output: path

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The Traveling Salesman Problem

Graph/Branches

1 5 10

PCB 442 55845 54778 54171ALI 535 2136 2140 2250EIL 101 675 679 675PR 1001 293180 288059 288059

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Logic Circuit Optimization

• Size optimization• Delay optimization• Energy optimization• …

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Workflow design

Logic Circuit Optimization

HC

TS SA TS SA

HCTS SA TS SA

TS SA TS SA

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Logic Circuit Optimization Sample circuit: IWLS93 Sequential execution (1 branch of the workflow) Literal optimization

Circuit #lit(in) MIS RAMBO Pert/Sim LogicOptC3540 2778 2145 1976 1876 1548

C7552 6583 3513 3521 2851 2125

C6288 7184 5237 4666 3766 3490

des 7657 6095 6145 5718 3664

apex6 1422 1260 1291 1086 788

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Logic Circuit Optimization Execute concurrently on Grid Execute a few times each (average results) Literal optimization

Circuit 1 branch 5 braches 10 branchesC3540 1548 1431 1388

C7552 2125 2149 2074

C6288 3490 3328 3295

des 3664 3611 3573

apex6 788 774 765

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Support the evolution-based heuristic. Provide more sophisticated workflow such

as loop, branching and parallel branches.

Future Work and Conclusion

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Thanks for your attention

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Question!?

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PCB442

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PCB442 – 1 branch: Fitness = 55845

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PCB442 – 5 branch: Fitness = 54778

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PCB442 – 10 branch: Fitness = 54171

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ALI535

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ALI535 – 1 branch: Fitness = 2136

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ALI535 – 5 branch: Fitness = 2140

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ALI535 – 10 branch: Fitness = 2250

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EIL101

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EIL101 – 1 branch: Fitness = 675

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EIL101 – 5 branch: Fitness = 679

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EIL101 – 10 branch: Fitness = 679

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PR1002

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PR1002 – 1 branch: Fitness = 293180

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PR1002 – 5 branch: Fitness = 288059

HPSC 2009 39

PR1002 – 10 branch: Fitness = 288059

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Literal Optimization Result

Optimize

count.blif – IWLS93