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New Math Methods and Perlmutter HPC Combine to Deliver Record-Breaking ML Algorithm
Using the Perlmutter supercomputer at NERSC, researchers at Berkeley Lab have devised a new mathematical method for analyzing extremely large datasets—and, in the process, demonstrated proof of principle on a record-breaking dataset of more than five million points. Read More »
Improving Training for Scientific Machine Learning
In the world of scientific machine learning (SciML), scientists are beginning to embrace the use of neural networks as a way to accelerate simulations. At the heart of deep learning algorithms, neural networks provide a mechanism to encode complex dependency structures, using many connected node layers to transform data into learned features to be used for a wide range of scientific tasks. Read More »
Berkeley Lab Works Toward a Connected Future for Science
Superfacility is a conceptual model of seamless connection between experimental facilities and high performance computing resources—an integrated and automated system for gathering, transporting, and analyzing scientific data in real time. Berkeley Lab is working to standardize, automate, and scale up the processes needed for superfacility onsite, across the U.S. Department of Energy (DOE), and beyond. Read More »
CS Area Research Shines at 2023 Postdoc Symposium
Eighteen Berkeley Lab postdoctoral researchers from across the Computing Sciences Area brought their A-game to this year’s CS Area Postdoc Symposium. Read More »
Berkeley Lab Researchers Share Expertise at SIAM’s CSE23
The biennial SIAM CSE23 conference will be held February 27–March 3 in Amsterdam. Berkeley Lab will be well-represented at the meeting. Read More »
The Most Advanced Bay Area Earthquake Simulations Will be Publicly Available
A collaboration involving scientists and computing resources from Berkeley Lab and the simulation software EQSIM is releasing the most accurate and detailed earthquake simulations to date, which will initially capture earthquake motions across the San Francisco Bay Area and later expand to other regions. Read More »
WarpX Code Shines at the Exascale Level
The WarpX project has spent the last six years creating a novel, highly parallel, and highly optimized single-source simulation code for modeling plasma-based particle colliders on cutting-edge exascale supercomputers, with broad importance for other accelerators and related problems. Read More »
Berkeley Lab Scientists Create Machine Learning Pipeline for Interpreting Large Tomography Datasets
A group of Berkeley Lab scientists has developed and tested several machine learning techniques organized in a learning pipeline to improve the interpretation of increasingly large cryo-ET datasets. Read More »
Berkeley Lab’s Ushizima Honored with PMWC Pioneer Award
Berkeley Lab’s Daniela Ushizima was recognized with PMWC Pioneer Award for constructing “a new and reliable technique for diagnosing Alzheimer’s disease and measuring the efficacy of experimental treatments.” Read More »
Perlmutter Results Show Progress in Quantum Information Science
As the first phase of NERSC's Quantum Information Sciences project [email protected] closes, it is already bearing fruit in the form of early science results and collaborations across the field. Read More »