1. 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

2. 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

3. Experimental results