Uintah is a computational framework for fluidstructure interaction problems using a combination of the ICE fluid flow algorithm, adaptive mesh refinement (AMR) and MPM particle methods. Uintah uses domain decomposition with a taskgraph approach for asynchronous communication and automatic message generation. The Uintah software has been used for a decade with its original task scheduler that ran computational tasks in a predefined static order. In order to improve the performance of Uintah for petascale architecture, a new dynamic task scheduler allowing better overlapping of the communication and computation is designed and evaluated in this study. The new scheduler supports asynchronous, out-of-order scheduling of computational tasks by putting them in a distributed directed acyclic graph (DAG) and by isolating task memory and keeping multiple copies of task variables in a data warehouse when necessary. A new runtime system has been implemented with a two-stage priority queuing architecture to improve the scheduling efficiency. The effectiveness of this new approach is shown through an analysis of the performance of the software on large scale fluid-structure examples.
The Uintah Software framework was developed to provide an environment for solving fluid-structure interaction problems on structured adaptive grids on large-scale, long-running, data-intensive problems. Uintah uses a combination of fluid-flow solvers and particle-based methods for solids together with a novel asynchronous task-based approach with fully automated load balancing. Uintah's memory use associated with ghost cells and global meta-data has become a barrier to scalability beyond O(100K) cores. A hybrid memory approach that addresses this issue is described and evaluated. The new approach based on a combination of Pthreads and MPI is shown to greatly reduce memory usage as predicted by a simple theoretical model, with comparable CPU performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.