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HP-CONCORD Paves the Way for Scalable Machine Learning in HPC
A team of Berkeley Lab researchers has demonstrated how a new parallel algorithm called HP-CONCORD can help address some of the most challenging problems in data-driven science. Read More »
AMReX Co-Design Center Helps Five ECP Projects Hit Performance Goals
The AMReX Co-Design Center makes available a state-of-the-art AMR infrastructure with the functionality that five ECP application projects and other AMR applications use to be able to effectively take advantage of current and future architectures. Read More »
Berkeley Lab to Build an Advanced Quantum Computing Testbed
The U.S. Department of Energy announced that Berkeley Lab will receive $30 million over five years to build and operate an Advanced Quantum Testbed (AQT). Researchers will use the testbed to explore superconducting quantum processors and evaluate how these emerging quantum devices can be utilized to advance scientific research. Read More »
Berkeley Lab to Push Quantum Information Frontiers With New Programs in Computing, Physics, Materials, and Chemistry
A series of DOE Office of Science awards, announced today, will enable Berkeley Lab to accelerate the development of quantum computing. Berkeley Lab Computing Sciences staff will play leading roles in three of the awards. Read More »
At Biden Summit, CRD's Ushizima Discusses Using Machine Learning to Improve Cancer Detection
Dani Ushizima, a staff scientist in the Computational Research Division who has adapted algorithms used in materials research to scan for cervical cancer, described her research in a panel discussion at the Sept. 21 East Bay Biden Cancer Community Summit. Read More »
Berkeley Lab Researchers Co-Author IARIA’s Best Paper
A paper co-authored by Berkeley Lab’s Esmond Ng and Chao Yang won best paper at this year’s International Academy, Research, and Industry Association’s (IARIA’s) Computation Tools 2018 conference in Barcelona, Spain. The paper, Deep Learning: A Tool for Computational Nuclear Physics, describes how feed-forward artificial neural networks can be used to predict the properties of atomic nuclei. Read More »
Berkeley Lab, BIDS Take on Big Data
The BIDS ecosystem comprises an impressive network of Fellows, including some who are Berkeley Lab scientists. This month, several Berkeley Lab-BIDS Fellows are organizing two of events to share their data-science expertise: Machine Learning for Science (ML4Sci) Workshop and the California Water Data Hackathon. Read More »
Exascale Computing Project Spotlights ExaGraph and STRUMPACK/SuperLU Collaboration
Results from a collaboration between the Exascale Computing Project's Sparse Solvers Software Technology project and ExaGraph Co-Design Center showed that the parallel AWPM (approximate-weight perfect matching) code can run up to 2,500x faster than the sequential algorithm on 256 nodes of NERSC's Cori supercomputer. Read More »
Berkeley Lab Researchers Showcase Deep Learning for High Energy Physics at CHEP
In a plenary talk given at the recent CHEP conference, NERSC and CRD researchers presented findings from a study in which they demonstrated how generative adversarial networks can speed simulations in high energy physics studies. Read More »
High School Summer Student Sanjana Shah Is 'Posit-ive' About Coding
Sanjana Shah, a high school student from the Peninsula discovered her passion for programming when she attended a coding workshop for youth sponsored by a local tech company. She is also the first (and so far, the only) high school student CRD's David Donofrio has ever hired through the Computing Sciences Summer Student program. Read More »
