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CCR Applied Mathematician Wins DOE Early Career Research Award

July 29, 2022 • CCR researcher Pete Bosler won a Department of Energy Office of Science Early Career Research Award of up to $500,000 annually for five years. The Early Career Research Program, now in its 13th year, is designed to provide support to researchers during their early career years. This year, DOE awarded...

CCR Researcher Receives EO Lawrence Award

July 28, 2022 • Quantum information scientist Andrew Landahl received a 2021 Ernest Orlando Lawrence Awards, the U.S. Department of Energy’s highest scientific mid-career honor. Landahl was recognized for his “groundbreaking contributions to quantum computing, including the invention of transformational quantum error correction protocols and decoding algorithms, for scientific leadership in the development of...

CCR scientists use gate set tomography to probe inner workings of quantum computers

January 1, 2022 • Two papers published in the journal Nature — one coauthored by Sandia researchers — used a Sandia technique called gate set tomography (GST) to demonstrate logic operations exceeding the “fault tolerance threshold” of 99% fidelity in silicon quantum computing processors.  Spawned by a Sandia Early Career LDRD in 2012, GST...

Machine Learning Enables Large-Scale Quantum Electron Density Calculations

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.

Secure multiparty computation supports machine learning in sensor networks.

January 1, 2022 • The Cicada project is a collaboration between Sandia National Laboratories and the University of New Mexico to develop the necessary foundations for privacy-preserving machine learning in large networks of autonomous drones.  Their approach utilizes secure multiparty communication methods to protect information within decentralized networks of low-power sensors that communicate via...
Illustration of privacy-preserving communication over a network of autonomous drones.

SNL adds Discontinuous Galerkin visualization capability to EMPIRE and ParaView

January 1, 2022 • Sandia National Laboratories in collaboration with Kitware, Inc. added new capabilities to store and visualize Discontinuous Galerkin (DG) simulation results using the Exodus/ParaView workflow to support the EMPIRE plasma physics application.  The DG methods employed by EMPIRE were selected because of their natural conservation properties, support of shock capturing methods,...
Electron density of the 2D Triple Point Problem warped in the Z dimension to illustrate the discontinuity between elements.

Automated Ensemble Analysis in Support of Nuclear Deterrence Programs

June 1, 2021 • Managing the modeling-simulation-analysis workflows that provide the basis for Sandia’s Nuclear Deterrence programs is a requirement for assuring verifiable, documented, and reproducible results. The Sandia Analysis Workbench (SAW) has now been extended to provide workflow management through the final tasks of ensemble analysis using Sandia’s Slycat framework. This new capability...
An unsupervised ensemble analysis sub-workflow sits atop a larger NGW computational simulation workflow. In the background, a solid mechanics parameter study is being analyzed in Slycat.

Credibility in Scientific Machine Learning: Data Verification and Model Qualification

June 1, 2021 • The Advanced Simulation and Computing (ASC) initiative on Advanced Machine Learning (AML) aims to maximize near and long-term impact of Artificial Intelligence (AI) and Machine Learning (ML) technologies on Sandia’s Nuclear Deterrence (ND) program. In this ASC-AML funded project, the team is developing new approaches for assessing the quality of...
Workflow illustration of a machine learning classifier, trained on labeled time- or frequency-domain signals in order to classify new measurements, with uncertainty metrics.

Machine Learning for Xyce Circuit Simulation

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

QSCOUT / Jaqal at the Frontier of Quantum Computing

May 1, 2021 • DOE/ASCR is investing over 5 years in Sandia to build and host the Quantum Scientific Computing Open User Testbed (QSCOUT): a quantum testbed based on trapped ions that is available to the research community (led by Susan Clark, 5225). As an open platform, it will not only provide full specifications...

IDEAS PSE Computational Platform Wins 2020 R&D 100 Award

January 1, 2021 • The IDAES Integrated Platform is a comprehensive set of open-source Process Systems Engineering (PSE) tools supporting the design, modeling, and optimization of advanced process and energy systems. By providing rigorous equation-oriented modeling capabilities, IDAES helps energy and process companies, technology developers, academic researchers, and the DOE to design, develop, scale-up,...
IDAES Integrated Platform

Investigating Arctic Climate Variability with Global Sensitivity Analysis of Low-resolution E3SM.

January 1, 2021 •  As a first step in quantifying uncertainties in simulated Arctic climate response, Sandia researchers have performed a global sensitivity analysis (GSA) using a fully coupled ultralow-resolution configuration of the Energy Exascale Earth System Model (E3SM).  Coupled Earth system models are computationally expensive to run, making it difficult to generate the...
Ultra-low atmosphere grid (left) and ultra-low ocean grid (right).

Machine-Learned Interatomic Potentials Are Now Plug-And-Play in LAMMPS

January 1, 2021 • Researchers at Sandia and Los Alamos National Laboratories have discovered a new way to implement machine learning (ML) interatomic potentials in the LAMMPS molecular dynamics code. This makes it much easier to prototype and deploy ML models in LAMMPS and provides access to a vast reservoir of existing ML libraries. ...
LAMMPS

2020 Rising Stars Workshop Supports Women in Computational & Data Sciences

January 1, 2021 • Rising Stars in Computational & Data Sciences is an intensive academic and research career workshop series for women graduate students and postdocs. Co-organized by Sandia and UT-Austin’s Oden Institute for Computational Engineering & Sciences, Rising Stars brings together top women PhD students and postdocs for technical talks, panels, and networking...
Rising 2020 Stars

Slycat Enables Synchronized 3D Comparison of Surface Mesh Ensembles

December 1, 2020 • In support of analyst requests for Mobile Guardian Transport studies, researchers at Sandia National Laboratories have expanded data types for the Slycat ensemble-analysis and visualization tool to include 3D surface meshes.  Analysts can now compare sets of surface meshes using synchronized 3D viewers, in which changing the viewpoint in one...
Here we see 3 cloned viewers for each of 2 runs at timestep 400 (red and blue selected points). The clones are vertically matched between the 2 runs to display the same 3 variables: the cell-based variables of Von Mises and stress along the X-axis, and the first component of the point variable React. The top row is an example of a simulation using the fastest initial velocity value (blue scatterplot point), while the bottom row is an example of the slowest (red scatterplot point).

Sandia and Kitware Partner to Improve Performance of Volume Rendering for HPC Applications

November 1, 2020 • In collaboration with researchers at Sandia, Kitware developers have made significant performance improvements to volume rendering for large-scale applications. First, Kitware significantly improved unstructured-grid volume rendering.  In a volume-rendering example for turbulent flow with 100 million cells on 320 ranks on a Sandia cluster, the volume rendered in 8 seconds...
The image shows an unstructured volume-rendered Q-criterion field for a Reynolds # ~10,000 turbulent impinging jet. The performance improvements enabled rendering (for the first time) of the full unstructured dataset (nearly 2 billion Hexahedral elements). The rendering of this image was supported by the ASC LSCI portfolio.

CCR Researcher Discusses IO500 on Next Platform TV

September 1, 2020 • CCR system software researcher Jay Lofstead appeared on the September 3rd episode of “Next Platform TV” to discuss the IO500 benchmark, including how it is used for evaluating large- scale storage systems in high-performance computing (HPC) and the future of the benchmark. Jay’s discussion with Nicole Hemsoth of the Next...
CCR Blocks

CCR Researcher Discusses Ceph Storage on Next Platform TV

July 1, 2020 • CCR system software researcher Matthew Curry appeared on the June 22nd episode of “Next Platform TV” to discuss the increased use of the Ceph storage system in high-performance computing (HPC). Matthew’s interview with Nicole Hemsoth of the Next Platform starts at the 18:40 mark of the video. In the interview,...
CCR Blocks

Key Numerical Computing Algorithm Implemented on Neuromorphic Hardware

July 1, 2020 • Researchers in Sandia’s Center for Computing Research (CCR) have demonstrated using Intel’s Loihi and IBM’s TrueNorth that neuromorphic hardware can efficiently implement Monte Carlo solutions for partial differential equations. CCR researchers had previously hypothesized that neuromorphic chips were capable of implementing critical Monte Carlo algorithm kernels efficiently at large scales,...

Sandia Researchers Collaborate with Red Hat on Container Technology

July 1, 2020 • Sandia researchers in the Center for Computing Research collaborated with engineers from Red Hat, the world’s leading provider of open source solutions for enterprise computing, to enable more robust production container capabilities for high-performance computing (HPC) systems. CCR researchers demonstrated the use of Podman, which allows ordinary users to build...

Sandia-led Earth System Modeling Project Featured in ECP Podcast

July 1, 2020 • CCR researcher Mark Taylor was interviewed in a recent episode of the “Let’s Talk Exascale” podcast from the Department of Energy’s Exascale Computing Project (ECP). Taylor leads the Energy Exascale Earth System Model – Multiscale Modeling Framework (E3SM-MMF) subproject, which is working to improve the ability to simulate the water...

Sandia Covid-19 Medical Resource Modeling

May 1, 2020 • As part of the Department of Energy response to the novel coronavirus pandemic of 2020, Sandia personnel developed a model to predict medical resources needed, including medical practitioners (e.g. ICU nurses, physicians, respiratory therapists), fixed resources (regular or ICU beds and ventilators), and consumable resources (masks, gowns, gloves, etc.) Researchers...
Figure 1. Resource needs over time with a range of uncertainty

Sandia to receive Fujitsu supercomputer processor

May 1, 2020 • This spring, CCR researchers anticipate Sandia becoming one of the first DOE laboratories to receive the newest A64FX Fujitsu processor, a Japanese Arm-based processor optimized for high-performance computing.The 48-core A64FX processor was designed for Japan’s soon-to-be-deployed Fugaku supercomputer, which incorporates high-bandwidth memory. It also is the first to fully utilize...
A64FX Processor

Sandia-led Supercontainers Project Featured in ECP Podcast

April 1, 2020 • As the US Department of Energy’s (DOE) Exascale Computing Project (ECP) has evolved since its inception in 2016, what’s known as containers technology and how it fits into the wider scheme of exascale computing and high-performance computing (HPC) has been an area of ongoing interest in its own right within...

Steve Plimpton Awarded the 2020 SIAM Activity Group on Supercomputing Career Prize

February 1, 2020 • Steve Plimpton has been awarded the 2020 Society for Industrial and Applied Mathematics (SIAM) 2020 Activity Group on Supercomputing Career Prize.  This prestigious award is given every two years to an outstanding researcher who has made broad and distinguished contributions to the field of algorithm development for parallel scientific computing. ...
Steve Plimpton
Results 1–25 of 125