Publications

Abstract not provided.

Near-term solutions are needed to allow for flexible engagement in future nuclear arms control discussions. This project developed a method for implementing an information barrier (IB) on commercial systems, shortening the research and development lifecycle for warhead verification technologies while offering improved and inherently flexible capabilities. The crux of the verification challenge remains the difficulty in developing an authenticatable IB which prevents sensitive host country information from inadvertent transmission to an inspector. Many concepts for IB’s rely on dedicated “trusted” processor modules developed with dedicated custom radiation detection systems and associated algorithms. Without a priori knowledge of the treaty item, the parameter space for measurements can be nearly infinite and robustness against spoofing without the ability to view sensitive data is key. This project has produced an unclassified framework capable of ingesting data from common gamma detectors and identifying the presence of weapons grade nuclear material at over 90% accuracy.

The basic building block of a distributed-memory cluster or supercomputer is a node. Each node includes a host, which is a processor (xPU) + memory hierarchy. The host can communicate with other hosts via its NIC (network interface controller). A network connects the nodes. The nodes may be arranged in some topology, which determines the network’s carrying capacity and cost.

Abstract not provided.

Abstract not provided.

Abstract not provided.

ALEGRA is a multiphysics finite-element shock hydrodynamics code, under development at Sandia National Laboratories since 1990. Fully coupled multiphysics capabilities include transient magnetics, magnetohydrodynamics, electromechanics, and radiation transport. Importantly, ALEGRA is used to study hypervelocity impact, pulsed power devices, and radiation effects. The breadth of physics represented in ALEGRA is outlined here, along with simulated results for a selected hypervelocity impact experiment.

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.

Centered on modern C++ and the SYCL standard for heterogeneous programming, Data Parallel C++ (dpc++) and Intel's oneAPI software ecosystem aim to lower the barrier to entry for the use of accelerators like FPGAs in diverse applications. In this work, we consider the usage of FPGAs for scientific computing, in particular with a multigrid solver, MueLu. We report on early experiences implementing kernels of the solver in DPC++ for execution on Stratix 10 FPGAs, and we evaluate several algorithmic design and implementation choices. These choices not only impact performance, but also shed light on the capabilities and limitations of DPC++ and oneAPI.

Centered on modern C++ and the SYCL standard for heterogeneous programming, Data Parallel C++ (dpc++) and Intel's oneAPI software ecosystem aim to lower the barrier to entry for the use of accelerators like FPGAs in diverse applications. In this work, we consider the usage of FPGAs for scientific computing, in particular with a multigrid solver, MueLu. We report on early experiences implementing kernels of the solver in DPC++ for execution on Stratix 10 FPGAs, and we evaluate several algorithmic design and implementation choices. These choices not only impact performance, but also shed light on the capabilities and limitations of DPC++ and oneAPI.

Abstract not provided.

This work, building on previous efforts, develops a suite of new graph neural network machine learning architectures that generate data-driven prolongators for use in Algebraic Multigrid (AMG). Algebraic Multigrid is a powerful and common technique for solving large, sparse linear systems. Its effectiveness is problem dependent and heavily depends on the choice of the prolongation operator, which interpolates the coarse mesh results onto a finer mesh. Previous work has used recent developments in graph neural networks to learn a prolongation operator from a given coefficient matrix. In this paper, we expand on previous work by exploring architectural enhancements of graph neural networks. A new method for generating a training set is developed which more closely aligns to the test set. Asymptotic error reduction factors are compared on a test suite of 3-dimensional Poisson problems with varying degrees of element stretching. Results show modest improvements in asymptotic error factor over both commonly chosen baselines and learning methods from previous work.

As the number of supported platforms for SNL software increases, so do the testing requirements. This increases the total time spent between when a developer submits code for testing, and when tests are completed. This in turn leads developers to hold off submitting code for testing, meaning that when code is ready for testing there's a lot more of it. This increases the likelihood of merge conflicts which the developer must resolve by hand -- because someone else touched the files near the lines the developer touched. Current text-based diff tools often have trouble resolving conflicts in these cases. Work in Europe and Japan has demonstrated that, using programming language aware diff tools (e.g., using the abstract syntax tree (AST) a compiler might generate) can reduce the manual labor necessary to resolve merge conflicts. These techniques can detect code blocks which have moved, as opposed than current text-based diff tools, which only detect insertions / deletions of text blocks. In this study, we evaluate one such tool, GumTree, and see how effective it is as a replacement for traditional text-based diff approaches.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.