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Results 201–225 of 402

Kokkos
Project • Modern high-performance computing (HPC) architectures have diverse and heterogeneous types of execution and memory resources. For applications and domain-specific libraries/languages to scale, port, and perform well on these architectures, their algorithms must be re-engineered for thread scalability and performance portability. The Kokkos programming system enables HPC applications and domain libraries...

Kokkos Kernels
Software • Kokkos Kernels is a software library of linear algebra and graph algorithms used across many HPC applications to achieve best (not just good) performance on every architecture. The baseline version of this library is written using the Kokkos Core programming model for portability and good performance. The library has architecture-specific optimizations...

Kurt Brian Ferreira
Staff Page • Scalable System Software. Biography Principal Member of Technical Staff My area of expertise is system software and resilience/fault-tolerance methods for large-scale, massively parallel, distributed-memory, scientific computing systems. I have designed and developed a number of innovative, high-performance, and resilient implementations of low-level system software for several HPC platforms including the Cray...
Kurt Ferreira

LAMMPS
Software • LAMMPS, an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator, is a classical molecular dynamics code with a focus on materials modeling. LAMMPS has potentials for solid-state materials (metals, semiconductors) and soft matter (biomolecules, polymers) and coarse-grained or mesoscopic systems. It can be used to model atoms or, more generically, as...

LAPIS
Software • Linear Algebra Performance through Intermediate Subprograms (LAPIS) is a compiler infrastructure based on MLIR for linear algebra that targets both high productivity and performance portability.

Laura Painton Swiler
Staff Page • Computing Research. Biography Laura has worked at Sandia since 1995. She is currently a senior scientist in the Center for Computing Research. Laura's research interests include uncertainty quantification for computational models, calibration of model parameters, sensitivity analysis, and model selection. Laura is a developer on the Dakota team: she develops...

Lekha Patel
Staff Page • Scientific Machine Learning. Biography I am a mathematician and computational statistician whose research interests broadly lie in the fields of statistical computing, statistical learning of partially-observed stochastic processes, stochastic PDEs for uncertainty quantification and Bayesian nonparametrics. Other areas of interest include spatial-temporal statistical modeling, extreme value analysis and Bayesian variable...

Luca Bertagna
Staff Page • Computational Science. Biography I'm interested in numerical methods for PDEs, and their efficient implementation on current and future HPC architectures. My background is in applied mathematics, including PDEs, functional analysis, numerical analysis, and HPC. I am also interested in modern software design and testing, with the goal to enhance code...

Machine Intelligence and Vis
Department • The Machine Intelligence and Visualization department conducts cutting-edge research in machine learning and artificial intelligence for national security applications, including the advanced visualization of data and results. Our mission is to research, develop, and deploy methods that are credible, robust, and help decision makers in a variety of domains. Specific...

Machine Learning Enables Large-Scale Quantum Electron Density Calculations
News Article, January 1, 2022 • Researchers at Sandia National Laboratories have developed a method for making previously impossible quantum chemistry calculations possible by using machine learning. A long standing problem in the quest to accurately simulate large molecular systems, like proteins or DNA, is the inability to perform accurate quantum chemistry calculations on these large...
The Euclidean Neural Network machine learning model accurately reproduces the quantum electron density for a 30 water molecule cluster.

Machine Learning for Xyce Circuit Simulation
News Article, June 1, 2021 • The Advanced Simulation and Computing (ASC) initiative to maximize near and long-term Artificial Intelligence (AI) and Machine Learning (ML) technologies on Sandia’s Nuclear Deterrence (ND) program funded a project focused on producing physics-aware machine learned compact device models suited for use in production circuit simulators such as Xyce. While the...
Proposed workflow for developing data-driven compact device models using Xyce and TensorFlow

Magneto-Hydro Dynamics
Focus Area • The magnetohydrodynamics (MHD) model describes the dynamics of charged fluids in the presence of electromagnetic fields. MHD models are used to describe important phenomena in the natural world (e.g., solar flares, astrophysical magnetic field generation, Earth's magnetosphere interaction with the solar wind) and in technological applications (e.g., spacecraft propulsion, magnetically confined plasma...

MALA
Software • MALA is a software package for building and deploying Scientific Machine Learning Models for electronic structure calculations, specifically density functional theory (DFT) calculations. DFT is one of the most widely used methods for simulating materials at a quantum level and predicting their properties, employed by researchers worldwide. MALA is open...

Mark Alan Taylor
Staff Page • Computational Science. Biography Mark Taylor is a mathematician who specializes in numerical methods for parallel computing and geophysical flows. He currently serves as Chief Computational Scientist for the DOE's Energy Exascale Earth System Model (E3SM) project. He developed the Hamiltonian structure preserving formulation of the spectral element method used in...

Mark Plagge
Staff Page • Postdoctoral Researcher. Biography Dr. Mark Plagge is a postdoctoral researcher in the Center for Computational Research at Sandia National Laboratories. With a background in high performance computing; modeling and simulations; and distributed systems, Mark is working to advance artificial intelligence and neuromorphic computing through AI model development, hardware accelerator simulations,...

Matthew Leon Curry
Staff Page • Scalable System Software. Biography I have a wide variety of research interests, mostly pertaining directly to data storage in supercomputing environments: Parallel and distributed storage systemsErasure coding and fault toleranceLow-level (block or object) storage devicesHeterogeneous computing (e.g., GPUs, accelerators) Education Ph.D. in Computer Science, University of Alabama at Birmingham, under...

Mauro Perego
Staff Page • Scientific Machine Learning. Biography Mauro focuses on the mathematical, numerical and computational treatment of Partial Differential Equations (PDE) that arise from various applications, PDE constrained optimization and recently in uncertainty quantification. Mauro has extensive experience with the Galerkin finite element method for discretizing PDEs, and he has been also working...

Member of the organizing committee
Award, February 1, 2010 – May 5, 2010 • Society/professional leadership, 2010 DOE Applied Math Program Meeting. Office of Science, DOE

Mesquite
Project • MESQUITE is a linkable software library that applies a variety of node-movement algorithms to improve the quality and/or adapt a given mesh. Mesquite uses advanced smoothing and optimization to: Untangle meshes,Provide local size control,Improve angles, orthogonality, and skew,Increase minimum edge-lengths for increased time-steps,Improve mesh smoothness,Perform anisotropic smoothing,Improve surface meshes, adapt...
Results 201–225 of 402