The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.0.3 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sen- sitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.
Proceedings of ScalA 2016: 7th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems - Held in conjunction with SC16: The International Conference for High Performance Computing, Networking, Storage and Analysis
We present a resilient task-based domain-decomposition preconditioner for partial differential equations (PDEs) built on top of User Level Fault Mitigation Message Passing Interface (ULFM-MPI). The algorithm reformulates the PDE as a sampling problem, followed by a robust regression-based solution update that is resilient to silent data corruptions (SDCs). We adopt a server-client model where all state information is held by the servers, while clients only serve as computational units. The task-based nature of the algorithm and the capabilities of ULFM complement each other to support missing tasks, making the application resilient to clients failing.We present weak and strong scaling results on Edison, National Energy Research Scientific Computing Center (NERSC), for a nominal and a fault-injected case, showing that even in the presence of faults, scalability tested up to 50k cores is within 90%. We then quantify the variability of weak and strong scaling due to the presence of faults. Finally, we discuss the performance of our application with respect to subdomain size, server/client configuration, and the interplay between energy and resilience.
The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncer- tainty in numerical model predictions. Version 3.0 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity anal- ysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.