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.