Container technology has provided greater software flexibility, reliability, ease of deployment, and portability—an ECP project aims to deliver it for exascale computing systems.
The work of a team at Stony Brook University could help speed the discovery of drugs to fight the novel coronavirus responsible for COVID-19.
Using high-performance computers, researchers at Princeton and the Princeton Plasma Physics Laboratory aim to quickly and accurately predict the onset of disruption disturbances involving the rapid releases of plasma that can cause structural damage in fusion reactors.
The WarpX project is developing an exascale application for plasma accelerator research that will pave the way for new breeds of virtual experiments.
The Energy Department's Exascale Computing Project has published a milestone report summarizing the status of its 30 Application Development subprojects (24 applications and six co-design centers) at the end of fiscal year 2019.
An article in CRSwire from HPE examines the many ways exascale computing is anticipated to solve problems previously out of reach.
Machine learning, artificial intelligence, and data analytics are converging with high-performance computing to advance scientific discovery.
LLVM within the US Department of Energy’s Exascale Computing Project is an open-source collaboration project that develops a compiler infrastructure.
Lawrence Livermore National Laboratory and Hewlett Packard Enterprise (HPE) have announced that AMD will be node supplier for the El Capitan supercomputer, and that the system is expected to exceed 2 exaflops.
Hewlett Packard Enterprise (HPE) and AMD have announced that the El Capitan supercomputer, planned for 2023, will have a speed of 2 exaflops to power complex scientific discovery for the US Department of Energy's National Nuclear Security Administration (NNSA).
Over the past several months, ECP’s HI team has been adapting its organizational structure and key personnel to prepare for the next phase of exascale hardware and software integration.
The Center for Efficient Exascale Discretizations (CEED) is a hub for high-order mathematical methods to increase application efficiency and performance.
