Global optimization problems can involve huge computational resources. The need to prepare, schedule and monitor hundreds of runs and interactively explore and analyze data is a challenging problem. Managing such a complex computational environment requires a sophisticated software framework which can distribute the computation on remote nodes hiding the complexity of the communication in such a way that scientist can concentrate on the details of computation. We present PARK, the computational job management framework being developed as a part of DANSE project, which will offer a simple, efficient and consistent user experience in a variety of heterogeneous environments from multi-core workstations to global Grid systems. PARK will provide a single environment for developing and testing algorithms locally and executing them on remote clusters, while providing user full access to their job history including their configuration and input/output. This paper will introduce the PARK philosophy, the PARK architecture and current and future strategy in the context of global optimization algorithms.

P Kienzle, N Patel, M McKerns, Parallel Kernels: An Architecture for Distributed Parallel Computing in Proceedings of the 8th Python in Science conference (SciPy 2009), G Varoquaux, S van der Walt, J Millman (Eds.), pp. 36-40

BibTeX entry

Copyright The content of the articles of the Proceedings of the Python in Science Conference is copyrighted and owned by their original authors.
Terms of use For republication or other use of the material published, please contact the copyright owners to obtain permission.