As simulations move to exascale machines, I/O and storage become a major limitation to scientific knowledge discovery. Exascale system concurrency is expected to grow by five or six orders of magnitude, yet I/O bandwidth is only expected to grow by two orders of magnitude. On exascale systems, the increasing I/O bottleneck will make it necessary for most simulation data to be analyzed in situ, or on the supercomputer while the simulation is running. Furthermore, to meet data bandwidth constraints, it will be necessary to sharply reduce the volume of data moved on the machine and especially the data that are exported to persistent storage. ALPINE provides a suite of in situ algorithms that will address these concerns by using adaptable data-driven techniques to downsample data, identify important features in the data, and move analysis of the data into the simulation process.
The current list of ALPINE algorithms is below. This documentation is a work in progress and some algorithms may not yet be fully documented.. _label_introduction:. ECP partner applications can find more information and contacts on the ECP ALPINE Confluence page.
- Adaptive Sampling
- Lagrangian Analysis
- Topological Analysis
- Rotation Invariant Pattern Detection
- Task Based Feature Detection