A multiresolution data model for improving simulation I/O performance

Numerical simulations running on very large High Performance Computer clusters still suffer from the I/O bottleneck. The cost of communication can overwhelm the cost of computation, and scales inversely with the number of processors used in the cluster. In previous work we have developed a multiresolution data model to help improve performance for visualizations of very large multi dimensional scientific data sets. In our approach, the data is represented as a multi level hierarchy. Reconstructive error analysis is used to identify regions in the data where the data loss is greatest. We have incorporated this data model into the OpenGGCM solar wind simulation environment. In this paper, we demonstrate that this approach can reduce the I/O and improve the overall performance of a large numerical simulation environment.1