Asynchronous genetic search for scientific modeling on large-scale heterogeneous environments

Use of large-scale heterogeneous computing environments such as computational grids and the Internet has become of high interest to scientific researchers. This is because the increasing complexity of their scientific models and data sets is drastically outpacing the increases in processor speed while the cost of supercomputing environments remains relatively high. However, the heterogeneity and unreliability of these environments, especially the Internet, make scalable and fault tolerant search methods indispensable to effective scientific model verification. The paper introduces two versions of asynchronous master-worker genetic search and evaluates their convergence and performance rates in comparison to traditional synchronous genetic search on both a IBM BlueGene supercomputer and using the MilkyWay@HOME BOINC Internet computing project 1. The asynchronous searches not only perform faster on heterogeneous grid environments as compared to synchronous search, but also achieve better convergence rates for the astronomy model used as the driving application, providing a strong argument for their use on grid computing environments and by the Milky Way@Home BOINC Internet computing project.

[1]  Enrique Alba,et al.  The influence of grid shape and asynchronicity on cellular evolutionary algorithms , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[2]  Giandomenico Spezzano,et al.  A Jxta Based Asynchronous Peer-to-Peer Implementation of Genetic Programming , 2006, J. Softw..

[3]  Andrew Lewis,et al.  Model Optimization and Parameter Estimation with Nimrod/O , 2006, International Conference on Computational Science.

[4]  David Abramson,et al.  Nimrod/G: an architecture for a resource management and scheduling system in a global computational grid , 2000, Proceedings Fourth International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region.

[5]  Isao Ono,et al.  A grid-oriented genetic algorithm framework for bioinformatics , 2009, New Generation Computing.

[6]  Enrique Alba,et al.  The exploration/exploitation tradeoff in dynamic cellular genetic algorithms , 2005, IEEE Transactions on Evolutionary Computation.

[7]  Enrique Alba,et al.  A Simple Cellular Genetic Algorithm for Continuous Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[8]  Heidi Jo Newberg,et al.  A Probabilistic Approach to Finding Geometric Objects in Spatial Datasets of the Milky Way , 2005, ISMIS.

[9]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[10]  Enrique Alba,et al.  Analyzing synchronous and asynchronous parallel distributed genetic algorithms , 2001, Future Gener. Comput. Syst..

[11]  Robert D. Blumofe,et al.  Scheduling multithreaded computations by work stealing , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.

[12]  Boleslaw K. Szymanski,et al.  Distributed and Generic Maximum Likelihood Evaluation , 2007, Third IEEE International Conference on e-Science and Grid Computing (e-Science 2007).

[13]  Andrew Lewis,et al.  An evolutionary programming algorithm for multi-objective optimisation , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[14]  Gul A. Agha,et al.  ACTORS - a model of concurrent computation in distributed systems , 1985, MIT Press series in artificial intelligence.

[15]  David P. Anderson,et al.  High-performance task distribution for volunteer computing , 2005, First International Conference on e-Science and Grid Computing (e-Science'05).

[16]  environmet.,et al.  JXTA : A Network Programming Environment , 2022 .

[17]  J. Berntsson,et al.  A convergence model for asynchronous parallel genetic algorithms , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[18]  Bu-Sung Lee,et al.  Efficient Hierarchical Parallel Genetic Algorithms using Grid computing , 2007, Future Gener. Comput. Syst..