1 / 24

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.

ama
Télécharger la présentation

Artificial Evolution: From Clusters to GRID

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related