The U.S. Department of Energy (DOE) announced $23.9 million in funding for ten projects in advanced scientific data management and visualization.
Tag: scientific computing
SLAC expands and centralizes computing infrastructure to prepare for data challenges of the future
A computing facility at the Department of Energy’s SLAC National Accelerator Laboratory is doubling in size, preparing the lab for new scientific endeavors that promise to revolutionize our understanding of the world from atomic to cosmic scales but also require handling unprecedented data streams.
DOE Invests $13.7 Million for Research in Data Reduction for Science
The U.S. Department of Energy (DOE) announced $13.7 million in funding for nine research projects that will advance the state of the art in computer science and applied mathematics.
Department of Energy Announces $15.1 Million for Integrated Computational and Data Infrastructure for Science Research
The U.S. Department of Energy (DOE) announced $15.1 million for three collaborative research projects, at five universities, to advance the development of a flexible multi-tiered data and computational infrastructure to support a diverse collection of on-demand scientific data processing tasks and computationally intensive simulations.
DOE Invests $13 Million in Research on Adapting Scientific Software to Run on Next-Generation Supercomputers
The U.S. Department of Energy (DOE) today announced more than $13 million for five advanced-computing projects across nine states, including $4.4 million to U.S. universities.
DOE Provides $28 Million To Advance Scientific Discovery Using Supercomputers
The U.S. Department of Energy (DOE) today announced $28 million in funding for five research projects to develop software that will fully unleash the potential of DOE supercomputers to make new leaps in fields such as quantum information science and chemical reactions for clean energy applications.
Department of Energy Announces $28.9 Million for Research to Develop Advanced Chemical Sciences Software
Today, the U.S. Department of Energy (DOE) announced $28.9 million in funding for nine research projects to advance the development of sophisticated software for the chemical sciences.
Argonne team collects Best Paper Award at SC20
The research described in the winning paper is focused on using a high-performance, iterative reconstruction system for noninvasive imaging at synchrotron facilities.
Creating the software that will unlock the power of exascale
Researchers nationwide are building the software and applications that will run on some the world’s fastest supercomputers. Among them are members of DOE’s Exascale Computing Project who recently published a paper highlighting their progress so far.
Break it down: A new way to address common computing problem
Researchers at the McKelvey School of Engineering at Washington University in St. Louis have developed a new algorithm for solving a common class of problem — known as linear inverse problems — by breaking them down into smaller tasks, each of which can be solved in parallel on standard computers.
Virtual lecture series finale connects interns to ongoing COVID-19 research
Students attending the last 2020 Office of Science Summer Internship Virtual Lecture Series seminar learned about how national laboratories are coming together to fight COVID-19.
CIO Amber Boehnlein Takes Computing up a Notch
Computer scientists, software developers and system administrators are coming together under one roof in the newly established Computational Sciences and Technology Division at the Department of Energy’s Thomas Jefferson National Accelerator Facility. Amber Boehnlein, Jefferson Lab’s chief information officer, has been promoted to associate director for computational sciences and technology, heading up the new division.
Argonne offers mentorship and resources to students in Department of Energy-sponsored graduate student research
As part of the Department of Energy’s Office of Science Graduate Student Research (SCGSR) Program, 62 graduate students were chosen to conduct thesis research across the national laboratory complex, including 12 students at Argonne.
IMSA High School Internship advances DUNE project and showcases unexplored potential of physics
Argonne National Laboratory’s Illinois Mathematics and Science Academy (IMSA) High School Internship Program has this year’s exceptionally bright high school students working on the Deep Underground Neutrino Experiment (DUNE)’s world-changing research.
Teamwork Triumphs at 2020 Illinois Regional Middle School Science Bowl
The U.S. Department of Energy’s Argonne National Laboratory Educational Programs and Outreach hosted the 2020 Illinois Regional Science Bowl Competition, where 15 different schools competed in trivia across a wide range of STEM topics.
University of Toledo engineering students as future STEM leaders
On Monday, January 13, engineering students from the University of Toledo’s Roy and Marcia Armes Engineering Leaderships Institute (ELI) visited Argonne National Laboratory to prepare themselves for the leadership challenges facing engineers.
Chicago Public School students go beyond coding and explore artificial intelligence with Argonne National Laboratory
The U.S. Department of Energy’s Argonne National Laboratory’s Educational Programs and Outreach department hosted Computer Science for All — Coding and Beyond, in December as a part of the Argonne National Laboratory, Chicago initiative.
Modeling Every Building in America Starts with Chattanooga
An ORNL team used the Titan supercomputer to model every building serviced by the Electric Power Board of Chattanooga—all 178,368 of them—and discovered that EPB could potentially save $11–$35 million per year by adjusting electricity usage during peak critical times.
Deep Learning Reveals Mysteries of Deep Space
The Science How do you determine the measurable “things” that describe the nature of our universe? To answer that question, researchers used CosmoFlow, a deep learning technique, running on a National Energy Research Scientific Computing Center supercomputer. They analyzed large,…