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Artificial Evolution: From Clusters to GRID

Artificial Evolution: From Clusters to GRID. Erol Şahin Cevat Şener Dept. of Computer Engineering Middle East Technical University Ankara. Darwinian Evolution. A population consists of a variety of individuals. The traits of individuals are determined by their genomes.

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Artificial Evolution: From Clusters to GRID

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  1. Artificial Evolution: From Clusters to GRID Erol Şahin Cevat Şener Dept. of Computer Engineering Middle East Technical University Ankara

  2. Darwinian Evolution • A population consists of a variety of individuals. • The traits of individuals are determined by their genomes. • Fitter individuals tend to produce more-than-average off-springs. • Off-springs are generated by a recombination of the genomes of the fitter individuals. Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  3. Artificial Evolution • Generate a population of solutions. • Evaluate the quality of each solution using a pre-defined “fitness function”. • Use the fitter solutions to generate more-than-average new solutions. • New solutions generated by a recombination of fitter solutions. Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  4. EVOLUTION Environment Individual Fitness PROBLEM SOLVING Problem Candidate Solution Quality The metaphor Fitness  chances for survival and reproduction Quality  chance for seeding new solutions Slide taken from Eiben and Smith’s presentation. Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  5. Evolutionary robotics • Challenge: How to design a controller that would make the robot to perform a desired task? • Manual controller design is often difficult/impossible • Realistic simulators are used to evaluate different controller alternatives. Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  6. Controller Evolutionary robotics Sensor data Chromosome Controller 010101 100111... Convert to controller parameters Use the controller in robots Actuator outputs Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  7. ........... Chrom.1: 1010001110... Chrom.2: 0011110101... ......... Population n Generation n SelectReproduceMutate Chrom.1: 0101011001... Chrom.2: 1100110111... ......... Generation n+1 Population n+1 ........... Evolving controllers Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  8. Physics Based Simulation • Pros • Faster and more reliable than experimentation with real robots • Realistic • Cons • High processing demand! Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  9. Single Machine Limitations • Computation required: • Solving Ordinary Differential Equations • Increasing complexity with more collisions • Time estimates for single computer: • Order of minutes for a single evaluation • For 100 chromosomes and 100 generations • Total time > a week on a single machine Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  10. Parallel Evolution System (PES) on a Cluster Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  11. PES Architecture • Server: Artificial Evolution • Clients: Fitness evaluation PES-C Client Application Server Application PES-S PES-C Client Application PES-C Client Application Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  12. PES Communication Model PES-S PES-C PES Network Adapter PES Network Adapter PVM PVM Host Host Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  13. PES-S Architecture PES-S Task Manager Task generator Artificial Evolution Server Application Best solutions Configuration Manager Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  14. PES-C Architecture Client Application Simulator Task PES-C Fitness Evaluator Fitness Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  15. Processor Load Balancing • Dynamic simulation • Varying number of collisions • Varying task complexity • Varying processor load Diamonds and Hexagons: tasks Solid lines: Start of new generation Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  16. Fault Tolerance • Processor 2 fails • Detected at ping at 15th sec • Task restart at 19th sec Red lines: PingBlue lines: GenerationNumbers: Task index Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  17. Efficiency & Speedup Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  18. Generation Gap for 128 Processors Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  19. Implementing PES on a Grid Two alternatives so far: • Porting PES as a whole from Clusters to Grid • Submitting only the clients onto Grid Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  20. Porting the whole PES 16 pvm PES-S,PES-C,PES-C,...,PES-C . . . PES-C PES-C PES-C PES-S Grid Engine . . . . . . pvmd pvmd pvmd pvmd Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  21. Porting the whole PES • Advantage • Easy implementation • Disadvantage • Requires that 16 nodes become available at the same time to start running Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  22. Only Clients PES-S JobArray: 1:15 PES-C Task Submission PES-C PES-C . . . Results PES-C Grid Engine Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  23. Only Clients • Disadvantage • Communication and synchronization setup between PES-S and Grid Engineis not straightforward • Advantage • Performance Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

  24. Questions/Comments? Ulusal GRID Çalıştayı, 21-22 Eylül 2005, Ankara

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