An Overview of Machine Learning for Scientific and High Performance Computing at Sandia
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Proceedings - 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science, SE4Science 2019
The modern HPC scientific software ecosystem is instrumental to the practice of science. However, software can only fulfill that role if it is readily usable. In this position paper, we discuss usability in the context of scientific software development, how usability engineering can be incorporated into current practice, and how software engineering research can help satisfy that objective.
Optimization Online Repository
Here, we develop a stochastic optimization model for scheduling a hybrid solar-battery storage system. Solar power in excess of the promise can be used to charge the battery, while power short of the promise is met by discharging the battery. We ensure reliable operations by using a joint chance constraint. Models with a few hundred scenarios are relatively tractable; for larger models, we demonstrate how a Lagrangian relaxation scheme provides improved results. To further accelerate the Lagrangian scheme, we embed the progressive hedging algorithm within the subgradient iterations of the Lagrangian relaxation. Lastly, we investigate several enhancements of the progressive hedging algorithm, and find bundling of scenarios results in the best bounds.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract not provided.
Proceedings - 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science, SE4Science 2019
The Sandia National Laboratories (SNL) Advanced Technology Development and Mitigation (ATDM) project focuses on R&D for exascale computational science and engineering (CSE) software. Exascale application (APP) codes are co-developed and integrated with a large number of 2^nd generation Trilinos packages built on top of Kokkos for achieving portable performance. These efforts are challenged by needing to develop and test on many unstable and constantly changing pre-exascale platforms using immature compilers and other system software. Challenges, experiences, and lessons learned are presented for creating stable development and integration workflows for these types of difficult projects. In particular, we describe automated workflows, testing, and integration processes as well as new tools and multi-team collaboration processes for effectively keeping a large number of automated builds and tests working on these unstable platforms.