Computational issues Issues Solutions Large time scale Use FSU’s 512 processor IBM 690p server Third fastest university owned supercomputer in the US Science-aware parallelization Predict regions likely to experience short time-scale phenomena and concentrate computational resources there Avoid fine granularity where possible Use Monte Carlo techniques for rare-event simulation when required, to avoid fine granularity Efficiently parallelizable through replication Faster versions of traditional parallelization techniques Stochastic versions of traditional domain decomposition techniques Trade computation for communication Mixed shared and distributed memory parallelization Optimize sequential component too Cache-aware computation Solutions Large time scale Small system size Fine grained parallelization High communication cost Adaptive computations Regions experiencing short time-scale phenomena simulated with a finer resolution Spatial decomposition and granularity change dynamically, and quickly, with time Need fast and efficient load balancing strategies

Preliminary results Nanocomposite simulation Model Matrix-nanotube interface modeled with springs An extra force term computed for atoms attached to springs Springs can break, requiring substantial increase in computations in that region Experimental parameters Nanotube with 1000 atoms Spring probability: 0.05 Probability of a spring breaking in an iteration: 0.01 Load increase factor due to spring break: 200 Disturbance region depth: 3 Number of time steps: 100 Spring Polymer matrix

Experimental results