March 12, 2018: A team of researchers from Berkeley Lab's CRD and JGI took one of the most popular clustering approaches in modern biology—the Markov Clustering algorithm—and modified it to run quickly, efficiently and at scale on distributed-memory supercomputers. Read More »
- Exascale and Beyond We're rethinking every aspect of scientific computing—hardware, software, algorithms, efficiency and networking—to address limits on processor speed, memory and energy consumption. The goal: more science per Watt.
- Data-Driven Science We develop tools and technologies and provide the critical networking and computer resources that help scientists turn enormous, widely shared, and complex data sets into discoveries.
- Scientific Complexity Engaging scientists in a holistic, team-based approach—from theory, observations and experiments to simulation and data analysis—is a grand challenge and goal of Computing Sciences at Berkeley Lab.
March 7, 2018: An innovative R&D project led by Berkeley Lab researchers that combines cybersecurity, machine learning and commercially available power system sensor technology to better protect the electric power grid has sparked interest from U.S. utilities, power companies and government officials. Read More »
March 1, 2018: Using the SciDAC developed SEDONA code and NERSC supercomputers, astrophysicists at Berkeley Lab and the University of Portsmouth discovered how to control the effects of "micolensing." Armed with this knowledge they believe they will be able to find 1000 strongly lensed Type Ia supernovae in real-time from LSST data--that's 20 times more than previous expectations. Read More »