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# 9th ITAB 2009 Parallel-MEGA

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A Parallel Implementation of a Multi-objective Evolutionary
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### 9th ITAB 2009 Parallel-MEGA

1. 1. A PARALLEL IMPLEMENTATION OF AMULTI-OBJECTIVE EVOLUTIONARYALGORITHMITAB 2009Christos C. Kannas, Christos A. Nicolaou,Constantinos S. PattichisUniversity of Cyprus and Noesis Cheminformatics
2. 2. OUTLINE 10/26/2011 Introduction Background  Graph Based Evolutionary Algorithms  Parallel Evolutionary Algorithms Methodology  Multi-objective Evolutionary Graph Algorithm  Parallel Multi-objective Evolutionary Graph Algorithm Results Conclusion Questions ??? 2
3. 3. INTRODUCTION 10/26/2011 Multi-objective Evolutionary Algorithms (MOEAs). Single-objective Problems  Single optimal solution. Multi-objective Problems  Set of equivalent solutions, Pareto-front. Parallel Evolutionary Algorithms  Parallel Processing:  General Purpose Graphical Processing Units (GPGPUs)  Multi- and Many-Core CPUs.  Clusters. 3
4. 4. BACKGROUND 10/26/2011 Graph Based Evolutionary Algorithms:  Graph G(V, E).  Mutations:  Flip Vertex/Edge.  Remove Vertex/Edge.  Add Vertex/Edge.  Problem specific mutations.  Add/Remove Ring.  Add/Remove/Exchange Fragment.  Crossover:  Recombination of Subgraphs. 4
5. 5. BACKGROUND 10/26/2011 Parallel Evolutionary Algorithms (PEAs)  Coarse-grained PEAs:  Several Subpopulations.  Isolation time.  Migration:  Uniformly at random.  Fitness based.  Migration Scheme:  Complete unrestricted net topology.  Ring topology.  Neighbourhood topology. 5
6. 6. BACKGROUND 10/26/2011 Parallel Evolutionary Algorithms (PEAs)  Coarse-grained PEA with Complete net topology. 6
7. 7. BACKGROUND 10/26/2011 Parallel Evolutionary Algorithms (PEAs)  Coarse-grained PEA with Ring topology. 7
8. 8. BACKGROUND 10/26/2011 Parallel Evolutionary Algorithms (PEAs)  Coarse-grained PEA with Neighbourhood topology. 8
9. 9. BACKGROUND 10/26/2011 Parallel Evolutionary Algorithms (cont.)  Fine-grained PEAs:  Master – Slave. 9
10. 10. METHODOLOGY 10/26/2011 Multi-objective Evolutionary Graph Algorithm (MEGA)  Chromosomes  Graphs.  MEGA Workflow:  Working Population.  Fitness Calculation.  Hard Filter.  Pareto Ranking.  Efficiency Calculation.  Parents Selection.  Evolve Parents. 10
11. 11. METHODOLOGY 10/26/2011 Multi-objective Evolutionary Graph Algorithm (cont.) 6. Evolve Working Parents Population 5. Select 1. Fitness Parents Calculation 4. 2. Hard Efficiency Filter Calculation 3. Pareto 11 Ranking
12. 12. METHODOLOGY 10/26/2011 Parallel Multi-objective Evolutionary Algorithm (PMEGA)  Python:  Threads  Global Interpreter Lock (GIL).  Processes  Spawning multiple processes (Our approach).  3rd Party add-ons, MPI4PY, PyCUDA, PyOpenCL.  Key facts:  A set of subpopulations. 2 subpopulations, although this is a parameter that can change.  A pool of processes. 2 cores  2 processes for simultaneous execution.  Execution path is the same as MEGA. 12
13. 13. METHODOLOGY 10/26/2011 Parallel Multi-objective Evolutionary Algorithm (PMEGA) (cont.)  PMEGA Workflow. 13
14. 14. RESULTS 10/26/2011 Testing PC:  Intel Core 2 Duo E8400 @ 3.0 GHz  4 Gbytes RAM Experiment Setup:  Population 100. (BioAssay 713)  Iterations 200.  5 Runs per experiment.  2 Objectives:  Similarity on 3 ligands, selective to ER-b.  Dissimilarity on 2 ligands, selective to ER-a.  3662 Building blocks, fragments taken from compounds of BioAssay 1211. 14
15. 15. RESULTS (CONT.) 10/26/2011 Time Results 15
16. 16. RESULTS (CONT.) 10/26/2011 Pareto Front from MEGA 16
17. 17. RESULTS (CONT.) 10/26/2011 Pareto Front from PMEGA 17
18. 18. RESULTS (CONT.) 10/26/2011 MEGA vs. PMEGA 18
19. 19. CONCLUSION 10/26/2011 Quality of solutions:  MEGA and PMEGA behave comparably. Using a better way to split the subpopulations might result in better results for PMEGA. Execution time:  PMEGA 1.6 times faster than MEGA on a 2 core CPU. 19
20. 20. QUESTIONS ??? 10/26/2011 20