Molecular dynamics simulation of large-scale carbon nanotubes on a shared-memory architecture
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Towards a grid simulation platform for dynamical systems
MOAS'07 Proceedings of the 18th conference on Proceedings of the 18th IASTED International Conference: modelling and simulation
Fault tolerant algorithms for heat transfer problems
Journal of Parallel and Distributed Computing
Towards a grid simulation platform for dynamical systems
MS '07 The 18th IASTED International Conference on Modelling and Simulation
Scheduling of tasks in the parareal algorithm
Parallel Computing
Hybrid dynamic iterations for the solution of initial value problems
Journal of Parallel and Distributed Computing
Application of reduce order modeling to time parallelization
HiPC'05 Proceedings of the 12th international conference on High Performance Computing
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Emerging computing environments, such as the Grid, promise enormous raw computational power. However, effective use of such platforms is often difficult, because conventional spatial decomposition leads to fine granularity, resulting in high communication overhead. We introduce the concept of guided simulations to parallelize along the time domain. Here, we use the fact that typically results of other simulations of closely related problems are available. In this approach, we automatically and dynamically determine a relationship between old simulations and the one being performed, and use this to parallelize along the time domain. We demonstrate the validity of this approach by applying the technique to an important application involving molecular dynamics simulation of nanomaterials. In this application, spatial decomposition is not effective due to the small size of the physical system. However, time parallelization is effective, since the granularity is much coarser. We also mention how this approach can be extended to make it inherently fault tolerant.