Towards a Middleware Framework for Dynamically Reconfigurable Scientific Computing

Computational grids are appealing platforms for the execution of large scale applications among the scientific and engineering communities. However, designing new applications and deploying existing ones with the capability of exploiting this potential still remains a challenge. Computational grids are characterized by their dynamic, non-dedicated, and heterogeneous nature. Novel application-level and middleware-level techniques are needed to allow applications to reconfigure themselves and adapt automatically to their underlying execution environments. In this paper, we introduce a new software framework that enhances the performance of Message Passing Interface (MPI) applications through an adaptive middleware for load balancing that includes process checkpointing and migration. Fields as diverse as fluid dynamics, materials science, biomechanics, and ecology make use of parallel adaptive computation. Target architectures have traditionally been supercomputers and tightly coupled clusters. This framework is a first step in allowing these computations to use computational grids efficiently.