Topic: Computational Science

LLNL has turned to AMD and Penguin Computing to upgrade a supercomputer to help in the fight against the novel coronavirus. The computer's name is... Corona.

Highlights include scalable deep learning, high-order finite elements, data race detection, and reduced order models.

Led by computational scientist Youngsoo Choi, the Data-Driven Physical Simulation reading group has been meeting biweekly since October 2019. The pandemic almost disbanded the group... until it turned into a virtual seminar series.

COVID-19 HPC Consortium scientists and stakeholders met virtually to mark the consortium’s one-year anniversary, discussing the progress of research projects and the need to pursue a broader organization to mobilize supercomputing access for future crises.

In recognition of March as International Women’s History Month, SC21 profiled six women doing trailblazing work, including LLNL's Hiranmayi Ranganathan.

The ATOM consortium, of which LLNL is part, announced the DOE’s Argonne, Brookhaven, and Oak Ridge national labs are joining the consortium to develop ATOM’s AI-driven drug discovery platform.

Research conducted on the Quartz supercomputer highlights findings made by scientists that reveal a missing aspect of the physics of hotspots in TATB and other explosives.

LLNL and IBM research on deep learning models to accurately diagnose diseases from x-ray images won the Best Paper award for Computer-Aided Diagnosis at the SPIE Medical Imaging Conference.

As part of the 50th anniversary of Virginia Tech’s computer science department, the university is featuring active and dynamic alumni—including LLNL computer scientist Ghaleb Abdulla.

Our researchers will be well represented at the virtual SIAM Conference on Computational Science and Engineering (CSE21) on March 1–5. SIAM is the Society for Industrial and Applied Mathematics with an international community of more than 14,500 individual members.

StarSapphire is a collection of scientific data mining projects focusing on the analysis of data from scientific simulations, observations, and experiments.

A multi-institutional team including LLNL is using Summit, America’s fastest supercomputer, to understand how certain proteins signal body cells to reproduce uncontrollably, triggering cancer.

Proxy apps serve as specific targets for testing and simulation without the time, effort, and expertise that porting or changing most production codes would require.

A machine learning model developed by a team of LLNL scientists to aid in COVID-19 drug discovery efforts is a finalist for the Gordon Bell Special Prize for HPC-Based COVID-19 Research.

The latest issue of LLNL's Science & Technology Review magazine highlights the work already accomplished with the Sierra supercomputer and what's to come.

At the 2020 LLNL Computing Expo, Jim Brase outlined the Lab’s predictive biology efforts for new therapeutics, HPC capabilities, and making those resources available to researchers.

LLNL will collaborate with Machina Labs to apply ML to aluminum sheet metal processing for aerospace and automotive applications. Five recently announced LLNL-led projects will be funded by HPC4EI.

Ruby, a 6-petaflop cluster, will be used for the stockpile stewardship mission, open science, and the search for therapeutic drugs and antibodies against SARS-CoV-2.

The scientific computing and networking leadership of 17 DOE national labs will be showcased at SC20, taking place Nov. 9-19 for the first time via a completely virtual format.

Funded by the CARES Act, LLNL's new computing cluster, Mammoth, will be used to perform genomics analysis, nontraditional simulations, and graph analytics required by scientists working on COVID-19.

The SAMRAI library is the code base in CASC for exploring application, numerical, parallel computing, and software issues associated with structured adaptive mesh refinement.

Funding by the CARES Act enabled LLNL and industry partners to more than double the speed of the Corona supercomputing cluster to in excess of 11 petaFLOPS of peak performance.

To solve a 100-year puzzle in metallurgy about why single crystals show staged hardening while others don’t, LLNL scientists performed atomistic simulations at the limits of supercomputing.

LLNL pairs 3D-printed human brain vasculature with computational flow simulations to understand tumor cell attachment to blood vessels, a step in secondary tumor formation during cancer metastasis.

LLNL and Duke University combine 3D bioprinting and computational flow models to analyze tumor cell behavior and the cells’ attachment to the vascular endothelium.