**Adaptive Load-Balancing for Force-Decomposition Based 3-Body Molecular
Dynamics Simulations in A Heterogeneous Distributed Environment with
Variable Number of Processors**

JV Sumanth and DR Swanson and H Jiang, 2007 INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING WORKSHOPS (ICPP), 196-205 (2007).

Molecular Dynamics (MD), a computationally intensive problem, is used by
researchers in various fields. The computational parallelism inherent in
this application can be exploited in parallel and distributed
environments. However in heterogeneous distributed environments such as
the Grid, 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 number of
interactions scheduled to each node. We propose one such scheduling
algorithm in this paper MD simulations based on the spatial-
decomposition (for short-range potentials) technique assuming
heterogeneous compute power and homogeneous links exist in the
literature. To the best of our knowledge, this paper is the first to
perform a block-level decomposition of the force-matrix for three-body
potentials in a distributed environment with heterogeneous compute power
with heterogeneous net-work links while exploiting the symmetries that
exist in a three-body force matrix. Our previous work **24** targeted MD
simulations using the Atom-Decomposition Method (Slice level
decomposition of the force-matrix) in a heterogeneous environment. 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 implement our algorithm and evaluate its effectiveness by
measuring the idle fraction which is a measure of the idle time
experienced by all compute clients at every time-step. This idle
fraction is a load-balance optimality measure that indicates how close
the load balancing is to the theoretical optimal of 0%. We find that
under most typical conditions, it is roughly, 6%. We also determine
potential enhancements to improve the idle fraction further.

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