Publications

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We discuss algorithm-based resilience to silent data corruption (SDC) in a task- based domain-decomposition preconditioner for partial differential equations (PDEs). The algorithm exploits a reformulation of the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to SDC. The implementation is based on a server-client model where all state information is held by the servers, while clients are designed solely as computational units. Scalability tests run up to ~ 51 K cores show a parallel efficiency greater than 90%. We use a 2D elliptic PDE and a fault model based on random single bit-flip to demonstrate the resilience of the application to synthetically injected SDC. We discuss two fault scenarios: one based on the corruption of all data of a target task, and the other involving the corruption of a single data point. We show that for our application, given the test problem considered, a four-fold increase in the number of faults only yields a 2% change in the overhead to overcome their presence, from 7% to 9%. We then discuss potential savings in energy consumption via dynamics voltage/frequency scaling, and its interplay with fault-rates, and application overhead.

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We examine four parametrizations of the unit sphere in the context of material stability analysis by means of the singularity of the acoustic tensor. We then propose a Cartesian parametrization for vectors that lie a cube of side length two and use these vectors in lieu of unit normals to test for the loss of the ellipticity condition. This parametrization is then used to construct a tensor akin to the acoustic tensor. It is shown that both of these tensors become singular at the same time and in the same planes in the presence of a material instability. Furthermore, the performance of the Cartesian parametrization is compared against the other parametrizations, with the results of these comparisons showing that in general, the Cartesian parametrization is more robust and more numerically efficient than the others.

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The Enhanced Surveillance Sub-program has an annual NNSA requirement to submit a comprehensive report on all our fiscal year activities right after the start of the next calendar year. As most of you know, we collate all of our PI task submissions into a single volume that we send to NNSA, our customers, and use for other programmatic purposes. The functional objective of this report is to formally document the purpose, status, and accomplishments and impacts of all our work. For your specific submission, please follow the instructions described below and use the template provided. These are essentially the same as was used last year. We recognize this report may also include information on specific age-related findings that you will provide again in a few months as input to the Stockpile Annual Assessment process (e.g., in the submittal of your Component Assessment Report). However, the related content of your ES AR input should provide an excellent foundation that can simply be updated as needed for your Annual Assessment input.

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