A team of Argonne scientists has leveraged artificial intelligence to train computers to keep up with the massive amounts of X-ray data taken at the Advanced Photon Source.
Researchers will be able to design their own computer accelerators for faster analysis of large datasets
An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus.
Six Argonne scientists receive Department of Energy’s Early Career Research Program Awards.
Now open for applications, Argonne’s Margaret Butler Fellowship in Computational Science offers an opportunity for one postdoc to work at the forefront of scientific computing at the Argonne Leadership Computing Facility.
Deborah Frincke, one of the nation’s preeminent computer scientists and cybersecurity experts, serves as associate laboratory director of ORNL’s National Security Science Directorate.
Using Argonne’s high-performance computing resources, researchers developed a new design for Caterpillar’s engines that could improve fuel efficiency while reducing harmful emissions.
Lawrence Livermore National Laboratory (LLNL), IBM and Red Hat are combining forces to develop best practices for interfacing high-performance computing (HPC) schedulers and cloud orchestrators, an effort designed to prepare for emerging supercomputers that exploit cloud technologies.
With quantum chemistry, PNNL scientists are discovering how enzymes such as nitrogenase serve as natural catalysts that efficiently break apart molecular bonds to control energy and matter.
Collaborators use experiments, high-fidelity simulations and machine learning to deliver predictive tools to engine manufacturers.
Computers play an integral role in nearly every discipline of research today, giving scientists the ability to discover new drugs, develop new materials, forecast the impacts of climate change, and solve some of today’s most challenging problems.
In advance of Argonne’s Aurora exascale supercomputer, Duke University assistant professor Amanda Randles is leading a new study to analyze cancer metastasis using HARVEY, a code that simulates blood vessels within the human body.
The Coalition for Academic Scientific Computation, or CASC — a network of more than 90 research computing-focused organizations from academic institutions, national labs, and research centers around the U.S. — hopes to continue to bring this critical computing work to the forefront through its activities. Newly elected leadership and recent changes within CASC are helping the nonprofit organization excel in this mission.
The Argonne Leadership Computing Facility continues its efforts to build a community of scientists who can employ AI and data-intensive analysis at a scale that requires DOE supercomputers.
In a collaborative effort to “recover, recycle and reuse,” Argonne strengthens research that addresses pollution, greenhouse gases and climate change and aligns with new policies for carbon emission reduction.
ROCKVILLE, MD – One thing that makes SARS-CoV-2, the virus that causes COVID-19, elusive to the immune system is that it is covered in sugars called glycans.
Ten organizations have created a pipeline of artificial intelligence and simulation tools to narrow the search for drug candidates that can inhibit SARS-CoV-2.
ATPESC provides in-depth training on using supercomputers, including next-generation exascale systems, to facilitate breakthrough science and engineering.
Scientists used a supercomputer to perform one of the five largest cosmological simulations ever — the Last Journey. This simulation will provide crucial data for sky maps to aid leading cosmological experiments.
A cross-platform Argonne collaboration is optimizing a tool developed after Hurricane Maria to find essential connections between critical infrastructure that will help owners and operators plan for and mitigate a variety of potential hazards.
HPCwire magazine recognizes two Argonne teams for outstanding achievement in their use of high performance computing.
Kalyan R S Perumalla is a Distinguished Research and Development Staff Member at Oak Ridge National Laboratory, whose work on reversible computing for exascale computers also provides insights applicable to next generation programming.
The San Diego Supercomputer Center (SDSC) at UC San Diego announced that its new Expanse supercomputer formally entered service for researchers following a program review by the National Science Foundation (NSF), which awarded SDSC a grant in mid-2019 to build the innovative system.
PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.
ORNL’s Paul Kent, Dr. Bart Iddins and two teams were recognized for leadership and accomplishment in science, technology and mission support.
The new projects will use DOE’s leadership-class supercomputers to pursue transformational advances in science and engineering.
The Argonne Leadership Computing Facility’s internship program went virtual this year, providing students with an opportunity to work on real-world research projects that address issues at the forefront of scientific computing.
PNNL’s new Smart Power Grid Simulator, or Smart-PGsim, combines high-performance computing and artificial intelligence to optimize power grid simulations without sacrificing accuracy.
Reported in Nature Biotechnology, the known diversity of bacteria and archaea has been expanded by 44% through a publicly available collection of more than 52,000 microbial genomes from environmental samples resulting from a JGI-led collaboration involving more than 200 scientists around the world.
Argonne scientists will attend the virtual SC20 conference to share research advances in areas ranging from exascale computing and big data analysis to AI and quantum computing.
PNNL researchers and university collaborators have developed a system to ferret out questionable use of high-performance computing (HPC) systems.
To leverage emerging computing capabilities and prepare for future exascale systems, the Argonne Leadership Computing Facility, a DOE Office of Science User Facility, is expanding its scope beyond traditional simulation-based research to include data science and machine learning approaches.
As the future home to the Aurora exascale system, Argonne National Laboratory has been ramping up efforts to ready the supercomputer and its future users for science in the exascale era.
The annual Argonne Training Program on Extreme-Scale Computing went virtual this year, providing two weeks of instruction to ready attendees for science in the exascale era.
Building on decades of successful collaborations, Mississippi State University and the U.S. Department of Agriculture’s Agricultural Research Service celebrated the new “Atlas” supercomputer Wednesday [Sept. 30] with a virtual event.
For years the coating industry has been working on energy and environmentally friendly improvements to the automotive painting process. Toward this end, researchers in Lawrence Berkeley National Laboratory’s Computational Research Division are partnering with one of the world’s largest paint manufacturers through a new project that aims to couple advanced mathematics with HPC resources to model the paint drying process and guide the development of new energy-efficient coating systems for the auto industry.
The Argonne Leadership Computing Facility recently hosted a virtual workshop to help researchers prepare code for the extreme scale and unique architectures that characterize leadership-class supercomputers.
Registration is now open for Penn State’s Institute of Computational and Data Sciences’ (ICDS) 2020 Symposium. The two-day symposium will be held virtually Oct. 21-22 and will feature an interdisciplinary group of speakers and experts who will focus on both the challenges — and opportunities — of big data and data science.
Scientists are investigating how to equip quantum computers with artificial intelligence and machine learning approaches.
Scientists are preparing for the increased brightness and resolution of next-generation light sources with a computing technique that reduces the need for human calculations to reconstruct images.
The Department of Energy is supporting the development of both conventional exascale supercomputers and quantum computers. Each provide benefits that could transform scientific research.
Lawrence Livermore National Laboratory (LLNL) and artificial intelligence computer company Cerebras Systems have integrated the world’s largest computer chip into the National Nuclear Security Administration’s (NNSA’s) Lassen system, upgrading the top-tier supercomputer with cutting-edge AI technology.
The U.S. Department of Energy (DOE) named three National Laboratory scientists as DOE Office of Science Distinguished Scientists Fellows
Argonne scientists Michael Bishof, Maria Chan, Marco Govini, Alessandro Lovato, Bogdan Nicolae and Stefan Wild have received funding for their research as part of DOE’s Early Career Research Program.
To meet the needs of tomorrow’s supercomputers, the National Nuclear Security Administration’s (NNSA’s) Lawrence Livermore National Laboratory (LLNL) has broken ground on its Exascale Computing Facility Modernization (ECFM) project, which will substantially upgrade the mechanical and electrical capabilities of the Livermore Computing Center.
A research team, led by Argonne, is developing a new data navigation system called Mochi that will provide scientists with a menu of data services they can rapidly combine and customize to suit the particular needs of a specific science domain.
Argonne researchers lead highly detailed COVID-19 modeling efforts to understand how the virus spreads through populations.
The Argonne Leadership Computing Facility recently hosted a workshop to help researchers advance code development efforts for Argonne’s upcoming exascale system, Aurora.
Researchers using DOE supercomputers, including Argonne’s Theta, produced pivotal 3D simulations to elucidate the physics behind the collapse of massive stars.
Combining high-fidelity computer simulations with ultra-high-speed X-ray imaging, researchers at Lawrence Livermore National Laboratory have discovered a strategy for reducing or even eliminating defects in parts built through a common, laser-based metal 3D-printing process.