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3 National Labs Join ATOM Consortium for Drug Discovery

MARCH 29, 2021 — The Accelerating Therapeutics for Opportunities in Medicine (ATOM) consortium today announced the U.S. Department of Energy’s Argonne, Brookhaven and Oak Ridge national laboratories are joining the consortium to further develop ATOM’s artificial intelligence (AI)-driven drug discovery platform. The public-private ATOM consortium aims to transform drug discovery from a slow, sequential and high-risk process […]

Brookhaven’s Advanced Computing Lab Expands with DDN A3I Storage and Nvidia DGX-2

High performance storage vendor DDN, provider of AI and data management software and hardware solutions, today announced Brookhaven National Laboratory has selected DDN’s A3I AI400X all-NVME flash appliance storage for fast optimum experimental design for its Computational Science Initiative (CSI).  Brookhaven National Laboratory plans to use DDN products along with the Nvidia DGX-2 AI supercomputer to expand its project […]

Taking a Virtual Turn, ModSim 2020 Focuses on the AI Era

From the front lines of this year’s ModSim conference, Charity Plata, Computational Science Initiative, Communications, Brookhaven National Laboratory, send this report: Recently, the ninth annual Workshop on Modeling & Simulation of Systems and Applications, known as ModSim 2020 — usually a 2.5-day event held amid the picturesque backdrop of the University of Washington Botanic Gardens in […]

Podcast: Rewriting NWChem for Exascale

In this Let’s Talk Exascale podcast, researchers from the NWChemEx project team describe how they are readying the popular code for Exascale. The NWChemEx team’s most significant success so far has been to scale coupled-cluster calculations to a much larger number of processors. “In NWChem we had the global arrays as a toolkit to be able to build parallel applications.”

ExaLearn: The ECP Co-Design Center for Machine Learning

In this video from the HPC User Forum, Frank Alexander from Brookhaven National Laboratory presents: ExaLearn – ECP Co-Design Center for Machine Learning. “It is increasingly clear that advances in learning technologies have profound societal implications and that continued U.S. economic leadership requires a focused effort, both to increase the performance of those technologies and to expand their applications. Linking exascale computing and learning technologies represents a timely opportunity to address those goals.”

DOE Extending Quantum Networks for Long Distance Entanglement

Scientists from Brookhaven National Laboratory, Stony Brook University, and DOE’s Energy Sciences Network (ESnet) are collaborating on an experiment that puts U.S. quantum networking research on the international map. Researchers have built a quantum network testbed that connects several buildings on the Brookhaven Lab campus using unique portable quantum entanglement sources and an existing DOE ESnet communications fiber network—a significant step in building a large-scale quantum network that can transmit information over long distances.

Researchers Tune HPC Codes for Intel Xeon Phi at Brookhaven Hackathon

“The goal of this hands-on workshop was to help participants optimize their application codes to exploit the different levels of parallelism and memory hierarchies in the Xeon Phi architecture,” said CSI computational scientist Meifeng Lin. “By the end of the hackathon, the participants had not only made their codes run more efficiently on Xeon Phi–based systems, but also learned about strategies that could be applied to other CPU-based systems to improve code performance.”

Reconstructing Nuclear Physics Experiments with Supercomputers

For the first time, scientists have used HPC to reconstruct the data collected by a nuclear physics experiment—an advance that could dramatically reduce the time it takes to make detailed data available for scientific discoveries. “By running multiple computing jobs simultaneously on the allotted supercomputing cores, the team transformed 4.73 petabytes of raw data into 2.45 petabytes of “physics-ready” data in a fraction of the time it would have taken using in-house high-throughput computing resources, even with a two-way transcontinental data journey.”

Bringing Diversity to Computational Science

“Computing is one of the least diverse science, technology, engineering, and mathematics (STEM) fields, with an under-representation of women and minorities, including African Americans and Hispanics. Leveraging this largely untapped talent pool will help address our nation’s growing demand for data scientists. Computational approaches for extracting insights from big data require the creativity, innovation, and collaboration of a diverse workforce.”

NYU Hosts Advanced Computing for Competitiveness Forum on April 13

The New York University Center for Urban Science and Progress will host the Advanced Computing for Competitiveness Forum on April 13. Sponsored by the U.S. Council on Competitiveness, the day-long event will look at why “To out-compete is to out-compute.” The Council’s landmark Advanced Computing Roundtable (ACR) – formerly the High Performance Computing (HPC) Initiative – is the preeminent forum for experts in advanced computing to set a national agenda on how such technologies should be leveraged for U.S. comptitiveness. Advanced computing includes technologies such as high performance computing, artificial intelligence (AI), and the Internet of Things (IoT). ACR members represent industrial and commercial advanced computing users, hardware and software vendors and directors of academic and national laboratory advanced computing centers.”