CCR Applied Mathematician Wins DOE Early Career Research Award
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 83 scientists nationwide, including 27 from the national laboratories and a total of four from Sandia. Bosler’s proposal entitled “High Performance Adaptive Multiscale Simulation with Data-driven Scale Selective Subgrid Parameterizations” aims to explore multiscale simulations that, integrated, could combine individual raindrops, thunderstorms, and the entire global atmosphere, guided by data currently thought too fine to be used, that is, too small to be seen on a data grid, or in other words, subgrid.
See the Sandia news release for more details.
Mike Heroux Receives Outstanding Service Award
CCR researcher Mike Heroux was recently awarded the 2023 Outstanding Service and Contribution Award from the IEEE Computer Society Technical Committee on Parallel Processing. Heroux was recognized “For outstanding service to the high-performance computing community, and significant contributions to the performance and sustainability of scientific software.” The award is given annually to individuals who have had substantial impact on the field through major professional roles, volunteer activities, or other demonstrations of community leadership. The award was presented during a ceremony at the IEEE International Parallel and Distributed Processing Symposium in St. Petersburg, Florida. Mike is the founder and leader of the Trilinos solver project, has held numerous conference and committee leadership roles, and is currently the Director of Software Technology for the US DOE Exascale Computing Project.
June 9, 2023
CCR Researcher Receives EO Lawrence Award
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 quantum computing technology and quantum programming languages and for professional service to the quantum information science community.” He is the first person to receive the EO Lawrence Award in field of quantum information science.
See the Sandia news release for more information on both Sandia winners.
SNL adds Discontinuous Galerkin visualization capability to EMPIRE and ParaView
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, and strong spatial locality which are advantageous to solvers relevant to plasmas. However, Sandia’s traditional visualization workflow involving Exodus and ParaView did not support DG without substantial preprocessing. This effort introduced new data structures and visualization support to the software stack to support DG methods including high-order DG methods.
The Sandia team added key pieces of “DG metadata” in combination with the existing Exodus data structures to create a mesh that represents the discontinuities inherent in DG. Kitware then modified ParaView to recognize the DG metadata and use it to “explode” the mesh from a continuous Exodus mesh to a discontinuous internal representation. As part of this process, shared vertices are replicated to give elements independent vertex instances and additional vertices are added as necessary to match the basis order of the mesh to the DG fields. The result is a faithful visualization of the discontinuities that can be used for analysis and debugging. Figure 1 shows the electron density of the 2D Triple Point Problem warped in the Z dimension to illustrate the discontinuity between elements. Areas of the mesh with small discontinuities have a smooth appearance while areas with larger discontinuities have distorted elements and gaps between elements.
This effort has added DG capabilities to the visualization tools that analysts know, understand and trust. With this new capability available, other applications (eg. SPARC and Flexo) have the opportunity to visualize their DG results without requiring additional postprocessing of simulation results.
Secure multiparty computation supports machine learning in sensor networks.
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 radio frequency. These networks are resilient to the random failure of a small fraction of nodes, remain secure even if an adversary captures a small subset of nodes, and are capable of basic machine learning. This new capability will help address national security priorities such as physical security and command, control, and communications.
A video is available with more information on privacy-preserving machine learning at the Autonomy NM Robotics Lab: https://www.youtube.com/watch?v=GM_JuKrw4Ik
For more information on the Cicada software package, visit https://cicada-mpc.readthedocs.io
Machine Learning Enables Large-Scale Quantum Electron Density Calculations
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 systems. Sandia Truman Fellow Josh Rackers and his collaborators at UCLA, MIT, and École Polytechnique Fédérale de Lausanne were able to train a machine learning model to accurately predict the electron densities of large systems by providing the model with small-scale training examples. This was made possible by utilizing a new type of neural network algorithm, Euclidean Neural Networks, that are especially designed for 3D machine learning problems. The model is able to make quantum chemistry predictions for systems of thousands of atoms in under a second – a calculation that would take decades or more with current quantum chemistry programs.
An arXiv pre-print of this work is available at https://arxiv.org/abs/2201.03726
CCR scientists use gate set tomography to probe inner workings of quantum computers
![Image of The January 20th, 2022 Nature cover shows an artist's impression of the three-qubit silicon device that was created by UNSW physicists, then measured and validated by Sandia quantum information scientists [Image credit: Tony Melov, UNSW].](https://www.sandia.gov/app/uploads/sites/210/2022/06/chip-1.png)
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 has since been developed at Sandia’s ASCR-funded Quantum Performance Lab. Sandia scientists collaborated with Australian researchers at the University of New South Wales in Sydney to publish one of the Nature papers, showcasing a three-qubit system comprising two atomic nuclei and one electron in a silicon chip. In parallel, a group from Delft University of Technology in the Netherlands used Sandia’s pyGSTi software to demonstrate equally high-fidelity logic using electrons trapped in quantum dots.
For additional information, please see:
Madzik M.T., et al., Precision tomography of a three-qubit donor quantum processor in silicon. Nature 601, 348-353 (2022).
Nature News & Views (Jan. 20, 2022), “Silicon qubits move a step closer to achieving error correction”.
YouTube video on Quantum operations with 99% fidelity – the key to practical quantum computers.
Machine Learning for Xyce Circuit Simulation
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 original goal was only to make a demonstration of these capabilities, the team worked closely with Xyce developers to ensure the resulting product would be suitable for the already large group of Xyce users both internal and external to Sandia. This was done by extending the existing C++ general external interface in Xyce and adding Pybind11 hooks. The result is that with release 7.3 of Xyce, the ability to define machine learned compact device models entirely in Python (the most commonly used machine learning language) and use them with Xyce will be publicly available.
Credibility in Scientific Machine Learning: Data Verification and Model Qualification
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 machine learning predictions based on expensive/limited experimental data, with focus on problems in the nuclear deterrence (ND) mission domain. Guided by the classical Predictive Capability Maturity Model (PCMM) workflow for Verification, Validation, and Uncertainty Quantification (V&V/UQ), the project aims to rigorously assess the statistical properties of the input features when training a Scientific Machine Learning (SciML) model and examine the associated sources of noise, e.g., measurement noise. This will, in turn, enable the decomposition of output uncertainty into unavoidable aleatory part versus reducible model-form uncertainty. To improve Sandia’s stockpile surveillance analysis capabilities, the team uses signature waveforms collected using non-destructive functioning and identify the most-discriminative input features, in order to assess the quality of a training dataset. By further decomposing uncertainty into its aleatoric and epistemic components, the team will guide computational/sampling resources towards reducing the treatable parts of uncertainty. This workflow will enhance the overall credibility of the resulting predictions and open new doors for SciML models to be credibly deployed to costly or high stakes ND problems.
Automated Ensemble Analysis in Support of Nuclear Deterrence Programs
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 enhances multi-platform modeling-simulation workflows through the Next Generation Workflow (NGW) system. With the goal of providing end-to-end workflow capability to the nuclear deterrence programs, the new technology integrates Slycat and SAW. It fills the workflow gap between computational simulation results and post-processing tasks of ensemble analysis and the characterization of uncertainty quantification (UQ). This work compliments simulation data management while providing encapsulated, targeted sub-workflows for ensemble analysis, verification and validation, and UQ. The integration of Slycat management into SAW affords a common point of control and configuration. This connects analysis with modeling and simulation, and provides a documented provenance of that analysis. The heart of the work is a set of innovative NGW components that harvest ensemble features, quantities of interest (QoIs), simulation responses, and in situ generated images, videos, and surface meshes. These components are triggered on-demand by the workflow engine when the prerequisite data and conditions are satisfied. Executing from an HPC platform, the components apply those artifacts to generate parameterized, user-ready analyses on the Slycat server. These components can eliminate the need for analyst intervention to hand-process artifacts or QoIs. The technology automates data flow and evidence production needed for decision-support in quantification of margins and uncertainty. Finally, these components deliver an automated and repeatable shortcut to Slycat’s meta-analysis crucial for optimizing ensembles, evaluating parameter studies, and understanding sensitivity analysis.
QSCOUT / Jaqal at the Frontier of Quantum Computing
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 and control for the realization of all high level quantum and classical processes, it will also enable researchers to investigate, alter, and optimize the internals of the testbed and test more advanced implementations of quantum operations. To maximize the usability and impact of QSCOUT, Sandia researchers in 1400 (Andrew Landahl, 1425) have led the development of the Jaqal quantum assembly language, which has been publicly released in conjunction with a QSCOUT emulator. QSCOUT is currently hosting external user teams from UNM, ORNL, IBM, the University of Indiana, and the University of California at Berkeley for scientific discovery in quantum computing.
For more information visit https://www.sandia.gov/quantum/Projects/QSCOUT.html
May 2021
Investigating Arctic Climate Variability with Global Sensitivity Analysis of Low-resolution E3SM.
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 large ensembles required for uncertainty quantification. In this research an ultralow version of E3SM was utilized to tractably investigate parametric uncertainty in the fully coupled model. More than one hundred perturbed simulation ensembles of one hundred years each were generated for the analysis and impacts on twelve Arctic quantities of interest were measured using the PyApprox library. The parameter variations show significant impact on the Arctic climate state with the largest impact coming from atmospheric parameters related to cloud parameterizations. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM. The results will be used to inform model tuning work as well as targeted studies at higher resolution.
For more information on E3SM: https://e3sm.org/
IDAES PSE Computational Platform Wins 2020 R&D 100 Award
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, and analyze new PSE technologies and processes to accelerate advances and apply them to address the nation’s energy needs. The platform is based on and extends the Pyomo optimization modeling environment originally developed at Sandia. IDAES has taken the core optimization capabilities in Pyomo and not only built a domain-specific process modeling environment, but also expanded the core environment into new areas, including logic-based modeling, custom decomposition procedures and optimization algorithms, model predictive control, and machine learning methods.
The IDAES PSE Computational Platform is developed by the Institute for the Design of Advanced Energy Systems (IDAES) and was recently awarded a 2020 R&D 100 Award. Led by National Energy Technology Laboratory (NETL), IDAES is a collaboration with Sandia National Laboratories, Berkeley Lab, West Virginia University, Carnegie Mellon University, and the University of Notre Dame.
For more information on IDAES, see https://idaes.org
2020 Rising Stars Workshop Supports Women in Computational & Data Sciences
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 events. The workshop series began in 2019 with a two-day event in Austin, TX. Due to travel limitations associated with the pandemic, the 2020 Rising Stars event went virtual with a compressed half-day format. Nonetheless, it was an overwhelming success with 28 attendees selected from a highly competitive pool of over 100 applicants. The workshop featured an inspiring keynote talk by Dr. Rachel Kuske, Chair of Mathematics at Georgia Institute of Technology, as well as lightning-round talks and breakout sessions. Several Sandia managers and staff also participated. The Rising Stars organizing committee includes Sandians Tammy Kolda (Distinguished Member of Technical Staff, Extreme-scale Data Science & Analytics Dept.) and James Stewart (Sr. Manager, Computational Sciences & Math Group), as well as UT Austin faculty Karen Willcox (Director, Oden Institute) and Rachel Ward (Assoc. Professor of Mathematics).
For more information on Rising Stars, see https://risingstars.oden.utexas.edu
Slycat Enables Synchronized 3D Comparison of Surface Mesh Ensembles
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 viewer changes viewpoints in all the others. To illustrate this capability, the Slycat team performed an ensemble analysis for a material-modeling study that examines fracturing behavior in a plate after being impacted by a punch. Input parameters include plate and punch density, friction coefficient, Young’s modulus, and initial punch velocity. To compare different mesh variables over the same geometry, the analyst clones a mesh into multiple views, as shown in Figure 1. The two runs represent opposite extremes for the initial punch velocity, with the 3D viewers in the top row showing the fastest initial velocity, and the viewers in the bottom row showing the slowest. The mesh variables in the two rows are vertically matched top and bottom, so by comparing rows, you can compare the distinctly different stress behaviors of the extremes.
This new capability represents a significant advance in our ability to perform detailed comparative analysis of simulation results. Analyzing mesh data rather than images provides greater flexibility for post-processing exploratory analysis.
Sandia and Kitware Partner to Improve Performance of Volume Rendering for HPC Applications
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 using the new method, 122 seconds for the old method, making unstructured-grid visualization a viable in-situ option for applications. Second, Kitware created a new “resample-to-image” filter that uses adaptive-mesh refinement to calculate and resample the image to the smaller mesh with minimal visualization artifacts. The new filter reduces the amount of data required for visualization and provides a potential performance improvement (more testing is needed). These improvements were driven by Sandia researchers for the NNSA Advanced Simulation and Computing program in support of the P&EM, V&V, and ATDM ASC sub-elements as part of a Large-Scale Calculation Initiative (LSCI) project. Kitware funding was provided through a contract with the ASC/CSSE sub-element.
November 1, 2020
CCR Researcher Discusses IO500 on Next Platform TV
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 Platform starts at the 32:04 mark of the video. In the interview, Jay describes the origins of the IO500 benchmark and the desire to provide a standard method for understanding how well an HPC storage system is performing for different workloads and different storage and file system configurations. Jay also describes how the benchmark has evolved since its inception, as well as the influence of the benchmark, and the ancillary impacts of ranking IO systems. More details and the entire episode are here:
https://www.nextplatform.com/2020/09/03/next-platform-tv-for-september-3-2020/
Sandia-led Earth System Modeling Project Featured in ECP Podcast
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 cycle and processes around precipitation. The podcast and a transcript of the interview can be found here.
Sandia Researchers Collaborate with Red Hat on Container Technology
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 and run containers without needing the elevated security privileges of an administrator, on the Stria machine at Sandia. Stria is an unclassified version of Astra, which was the first petascale HPC system based on an Arm processor. While Arm processors have shown to be very capable for HPC workloads, they are not as prevalent in laptops and workstations as other processors. To address this limitation, Podman provides the ability to build containers directly on machines like Stria and Astra without requiring root-level access. This capability is a critical advancement in container functionality for the HPC application development environment. The CCR team is continuing to work with Red Hat on improving Podman for traditional HPC applications as well as machine learning and deep learning workloads. More details on this collaboration can be found here:
Key Numerical Computing Algorithm Implemented on Neuromorphic Hardware
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, and this study was the first to demonstrate that this approach could be used to approximate solutions to arrive at a steady-state PDE solution. This study formalized the mathematical description of PDEs into an algorithmic form suitable for spiking neural hardware and highlighted results from implementing this spiking Monte Carlo algorithm on Sandia’s 8-chip Loihi test board and the IBM TrueNorth chip at Lawrence Livermore National Laboratory. These results confirmed that the computational costs scale highly efficiently with model size; suggesting that spiking architectures such as Loihi and TrueNorth may be highly desirable for particle-based PDE solutions. This work was funded by Sandia’s Laboratory Directed Research and Development (LDRD) program and the DOE Advanced Simulation and Computing (ASC) program. The paper has been accepted to the 2020 International Conference on Neuromorphic Systems (ICONS) and is available at https://arxiv.org/abs/2005.10904
CCR Researcher Discusses Ceph Storage on Next Platform TV
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, Matthew describes the Stria system, which is an unclassified version of Astra, which was the first petascale HPC system based on the Arm processor. Matthew also describes the use of the Ceph storage system and some of the important aspects that are being tested and evaluated on Stria. More details and the entire episode are here.
Sandia to receive Fujitsu supercomputer processor
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 wide vector lanes that were designed around Arm’s Scalable Vector Extensions. These wide vector lanes make possible a type of data-level parallelism where a single instruction operates on multiple data elements arranged in parallel. Penguin Computer Inc. will deliver the new system — the first Fujitsu PRIMEHPC FX700 with A64FX processors. Sandia will evaluate Fujitsu’s new processor and compiler using DOE mini- and proxy-applications and will share the results with Fujitsu and Penguin. More details are available here.
Sandia Covid-19 Medical Resource Modeling
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 in Center 1400 developed a framework for performing uncertainty analysis on the resource model. The uncertainty analysis involved sampling 26 input parameters using the Dakota software. The sampling was performed conditional on the patient arrival streams, which were derived from epidemiology models and had a significant effect on the projected resource needs.
Using two of Sandia’s High Performing Computing clusters, the generated patient streams were run through the resource model for each of 3,145 counties in the United States, where each county-level run involved 100 samples per scenario. Three different social distancing scenarios were investigated. This resulted in approximately 900,000 individual runs of the medical resource model, requiring over 500 processor hours on the HPCs. The results included mean estimates per resource per county, as well as uncertainty in those estimates (e.g., variance, 5th and 95th quantile, and exceedance probabilities). Example results are shown in Figures 1-2. As updated patient stream projections become available from the latest epidemiology models, the analysis can be re-run quickly to provide resource projections in rapidly changing environments.
For more information on Sandia research related to COVID-19, please visit the COVID-19 Research website.
Sandia-led Supercontainers Project Featured in ECP Podcast
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 the HPC community.
Container technology has revolutionized software development and deployment for many industries and enterprises because it provides greater software flexibility, reliability, ease of deployment, and portability for users. But several challenges must be addressed to get containers ready for exascale computing.
The Supercontainers project, one of ECP’s newest efforts, aims to deliver containers and virtualization technologies for productivity, portability, and performance on the first exascale computing machines, which are planned for 2021.
ECP’s Let’s Talk Exascale podcast features as a guest Supercontainers project team member Andrew Younge of Sandia National Laboratories. The interview was recorded this past November in Denver at SC19: The International Conference for High Performance Computing, Networking, Storage, and Analysis.
Steve Plimpton Awarded the 2020 SIAM Activity Group on Supercomputing Career Prize
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. According to SIAM, the Career Prize recognizes Steve’s “seminal algorithmic and software contributions to parallel molecular dynamics, to parallel crash and impact simulations, and for leadership in modular open-source parallel software.”
Steve is the originator of several successful software projects, most notably the open-source LAMMPS code for molecular dynamics. Since its release in 2004, LAMMPS has been downloaded hundreds of thousands of times and has grown to become a leading particle-based materials modeling code worldwide. Steve’s leadership in parallel scientific computing has led to many opportunities for the Center for Computing Research to collaborate on high-performance computing projects both within and outside Sandia National Laboratories.
Karen Devine awarded the Society of Women Engineers 2019 Prism Award
Karen Devine has been awarded the Society of Women Engineers (SWE) 2019 Prism Award. According to SWE, “the Prism Award recognizes a woman who has charted her own path throughout her career, providing leadership in technology fields and professional organizations along the way.” Karen has been deservedly awarded this honor based on her contributions as a “computer science researcher, project leader, customer liaison, mentor and STEM outreach advocate.” Her contributions in delivering algorithms and high-quality software that improve the performance of engineering simulation codes at Sandia are particularly noteworthy.
Karen has been a trailblazer for open-source software practices and policies in Sandia. Now her software is “used in national laboratories, industry, and universities world-wide, with 4500+ downloads of just one of her software libraries.” Karen has demonstrated “strong and effective leadership of small software teams, multi-million dollar projects across many national laboratories, local STEM service projects, and international professional societies.” Karen will be presented with the Prism Award at SWE’s WE19 conference on November 7.
Sandia, PNNL, and Georgia Tech Partner on New AI Co-Design Center
Sandia National Laboratories, Pacific Northwest National Laboratory, and the Georgia Institute of Technology are launching a research center that combines hardware design and software development to improve artificial intelligence technologies. The Department of Energy Office of Advanced Scientific Computing Research (ASCR) will provide $5.5 million over three years for the research effort, called ARtificial Intelligence-focused Architectures and Algorithms (ARIAA). This new collaboration is intended to encourage researchers at the three institutions, each with their own specialty, to simulate and evaluate artificial intelligence hardware when employed on current or future supercomputers. Researchers also should be able to improve AI and machine-learning methods as they replace or augment more traditional computation methods. See this press release for more details.
Astra Supercomputer Team Wins NNSA Defense Programs Award of Excellence
The Astra Supercomputer Team was recently awarded an NNSA Defense Programs Award of Excellence “For excellence in design, acquisition, and integration of the Astra prototype System.” These prestigious annual awards are granted to National Security Enterprise teams and individuals across the NNSA complex to recognize significant contributions to the Stockpile Stewardship Program. The Astra team was one of two Sandia teams to receive an Exceptional Achievement Award at a recent ceremony that recognized a total of 31 teams and three individuals from Sandia. The team successfully delivered the first advanced prototype system for NNSA’s Advanced Simulation and Computing (ASC) Program, moving from requirements definition, through acquisition to delivery, integration, and acceptance of the large-scale computing system in less than twelve months. The Astra system is the world’s largest and fastest supercomputer based on Arm processors. The team is composed of managers, staff, and contractors from the Center for Computing Research, the Center for Computer Systems and Technology Integration, and the Information Technology Services Center.
CCR Researcher Ryan Grant Honored by Queen’s University
CCR Researcher Ryan Grant was recently recognized by his alma mater as one of the top 125 engineering alumni or faculty of Queen’s University during a celebration of the 125thanniversary of the Faculty of Engineering and Applied Science. The award recognizes the achievements of alumni and faculty who are outstanding leaders in their field and represent excellence in engineering. Winners were recognized in March during a ceremony at the university in Kingston, Ontario, Canada. Ryan received his Bachelor of Applied Science, Master of Science in Engineering, and Ph.D. in Computer Engineering from Queen’s, and he is a Principal member of technical staff in the Scalable System Software department with expertise in high-performance interconnect technologies.
CCR Researcher Jay Lofstead Co-Authors Best Paper at HPDC’19
CCR Researcher Jay Lofstead and his co-authors from the Illinois Institute of Technology have been awarded Best Paper at the recent 2019 ACM International Symposium on High- Performance Parallel and Distributed Computing. Their paper entitled “LABIOS: A Distributed Label-Based I/O System” describes an approach to supporting a wide variety of conflicting I/O workloads under a single storage system. The paper introduces a new data representation called a label, which more clearly describes the contents of data and how it should be delivered to and from the underlying storage system. LABIOS is a new class of storage system that uses data labeling and implements a distributed, fully decoupled, and adaptive I/O platform that is intended to grow in the intersection of High-Performance Computing and Big Data. Each year the HPDC Program Chairs select the Best Paper based on reviews and discussion among the members of the Technical Program Committee. The award is named in memory of Karsten Schwan, a professor at Georgia Tech who made significant and lasting contributions to the field of parallel and distributed computing.
Power API and LAMMPS Win R&D 100 Awards
Two CCR technologies have won 2018 R&D100 Awards. Each year, R&D Magazine names the 100 most technologically significant products and advancements, recognizing the winners and their organizations. Winners are selected from submissions from universities, corporations, and government labs throughout the world. This year’s winners include the Power API and LAMMPS. The Power API was also recognized with a Special Recognition Award for corporate social responsibility. Sandia garnered a total of five R&D100 Awards. The Power API is portable programming interface for developing applications and tools that can be used to control and monitor the power use of high-performance computing systems in order to improve energy efficiency. LAMMPS is a molecular dynamics modeling and simulation application designed to run on large-scale high performance computing systems. Winners were announced at a recent ceremony at the R&D 100 Conference.
Astra Supercomputer is Fastest Arm-Based Machine on Top 500 List
Sandia’s Astra is the world’s fastest Arm-based supercomputer according to the just released TOP500 list, the supercomputer industry’s standard. With a speed of 1.529 petaflops, Astra placed 203rd on a ranking of top computers announced at SC18, the International Conference for High Performance Computing, Networking, Storage, and Analysis, in Dallas. A petaflop is a unit of computing speed equal to one thousand million million (1015) floating-point operations per second. Astra achieved this speed on the High-Performance Linpack benchmark. Astra is one of the first supercomputers to use processors based on Arm technology. The machine’s success means the supercomputing industry may have found a new potential supplier of supercomputer processors, since Arm designs are available for licensing. More details are in this article.
Astra – An Arm-Based Large-Scale Advanced Architecture Prototype Platform
Astra, one of the first supercomputers to use processors based on the Arm architecture in a large-scale high-performance computing platform, is being deployed at Sandia National Laboratories. Astra is the first of a potential series of advanced architecture prototype platforms, which will be deployed as part of the Vanguard program that will evaluate the feasibility of emerging high-performance computing architectures as production platforms. The machine is based on the recently announced Cavium Inc. ThunderX2 64-bit Arm-v8 microprocessor. The platform consists of 2,592 compute nodes, of which each is 28-core, dual-socket, and will be at a theoretical peak of more than 2.3 petaflops, equivalent to 2.3 quadrillion floating-point operations (FLOPS), or calculations, per second. While being the fastest is not one of the goals of Astra or the Vanguard program in general, a single Astra node is roughly one hundred times faster than a modern Arm-based cellphone. More details are available here.
