Adaptive Load Balancing for Long-Range MD Simulations in A Distributed Environment

Molecular dynamics, a computationally intensive application is used by researchers in various fields. The inherent parallelism (Plimpton and Hendrickson, 1994) in the computations involved with this application can be exploited in parallel and distributed environments. However, in distributed environments such as the grid (Foster et al., 2001), the available resources, namely the network and computational power, are continually changing with respect to every available node. To optimally utilize these dynamic resources, a scheduler should be able to continually adapt to the changes and suitably vary the load scheduled to every available node. We propose one such scheduling algorithm. The proposed scheduling algorithm builds and continually updates a model of the distributed system, which it then uses to make decisions about how to optimally redistribute the load in the system at every time step of the MD simulation. The scheduling algorithm can additionally handle dynamic changes in the number of nodes available for computation at runtime. We then demonstrate the efficiency of our scheduling algorithm when applied to MD simulations in a distributed environment