Research and Development
As well as providing production computing services NERSC also participates in research and development activities in order to ensure the high performance computing systems of the future continue to meet the needs of scientists.
Alice Koniges (third from left) led the Computational Science and Engineering Petascale Initiative, which paired post-doctoral researchers with high-impact projects at NERSC. Post-docs pictured above are (from left) are Jihan Kim, Filipe Maia, Robert Preissl, Brian Austin, Wangyi (Bobby) Liu , Kirsten Fagnan and Praveen Narayanan. (Not pictured: Christos Kavouklis, Xuefei (Rebecca) Yuan) The Computational Science and Engineering Petascale Initiative at LBNL identified key application areas… Read More »
NERSC assess available HPC system solutions using a combination of application benchmarks and microbenchmarks. By understanding the requirements of the NERSC workload we drive changes in computing architecture that will result in better HPC system architectures for scientific computing in future generation machines. Read More »
Enabling Data-Driven Scientific Discovery at HPC Facilities June 18-19, 2014 California State University, East Bay Oakland Professional Development and Conference Center Trans Bay Center 1000 Broadway, Suite 109 Oakland, CA The DOE High Performance and Computing Operational Review (HPCOR) covered processes and practices for delivering facilities and services that enable high performance data-driven scientific discovery at the DOE national laboratories. The meeting was held in conjuction… Read More »
Running efficiently on future low-power, manycore system architectures will pose a major challenge to almost all scientific application codes. DOE supercomputer centers are working together now to plan and coordinate how they will help enable science teams take advantage of next-generation… Read More »
Massive Acceleration of New Techniques In Science with Scalable Algorithms Motivation Scalable Statistics and Machine Learning Algorithms are essential for extracting insights from Big Data. Our interdisciplinary team is trying to address a number of challenging analysis problems from a number of science domains at Lawrence Berkeley Lab (LBL). We are developing novel algorithms, and using state-of-the-art methods from High Performance Computing, Scientific Data Management and Parallel I/O… Read More »