Program Offices – DOE Office of Science

Optimizing the North American Power Grid to Improve Reliability and Support Grid Decarbonization

The ExaSGD project wants to enable the day-to-day operation of the national power grid to hold a large number of renewable power sources.

Let’s Talk Exascale Code Development: WDMapp—XGC, GENE, GEM

The latest in the code-for-Aurora series explores an app aimed at high-fidelity whole device modeling of magnetically confined fusion plasmas.

Applying Graph Algorithms to Solve Key Science Problems of Importance to the Nation

ExaGraph aims to leave a lasting legacy of algorithms, implementations, and graph-enabled applications for scientific discovery.

Berkeley Lab Deploys Next-Gen Supercomputer – Perlmutter

The National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory (Berkeley Lab) today formally unveiled the f

Let’s Talk Exascale Code Development: HACC

This time, ECP's special podcast series on preparing code for the Aurora exascale supercomputer takes a look at the cosmological code HACC.

Ensuring the Exascale Ecosystem Lands Successfully at Energy Department Facilities

The Exascale Computing Project Software Deployment at Facilities project tests and verifies software functionality and efficiency.

Let’s Talk Exascale Code Development: EQSIM

ECP’s special podcast series on preparing code for the Aurora exascale system begins by focusing on an earthquake risk assessment application.

Exploiting Exascale Computing to Make Earthquake Simulation Codes More Powerful

Simulating earthquake processes from end to end is key to being able to design bridges and buildings to be more resilient to earthquakes.

Supporting Scientific Discovery and Data Analysis in the Exascale Era

ECP's Data and Visualization portfolio is delivering data management software to store, save state, share, and facilitate the analysis of exascale data.

Collaborative Strength Enables the EXAALT Project to Optimize GPU Performance

The EXAALT project could bring atomistic materials predictions to the engineering scale and demystify materials design and synthesis.

E3SM-MMF: Forecasting Water Resources and Severe Weather with Greater Confidence

By exploiting the power and performance advantages of GPUs and exascale computing, researchers aim to better predict changes to Earth’s water cycles.

Flexible Package Manager Automates the Deployment of Software on Supercomputers

Spack (Supercomputer PACKage manager) is an R&D 100–Award winner because of its worldwide impact on high-performance computing.

Reducing the Memory Footprint and Data Movement on Exascale Systems

The ZFP software development effort is tackling the critical task of overcoming the performance cost of data movement for exascale computing.

Accelerating the Adoption of Container Technologies for Exascale Computing

Container technology has provided greater software flexibility, reliability, ease of deployment, and portability—an ECP project aims to deliver it for exascale computing systems.

Creating the Ability to Design Smaller, Cheaper Particle Accelerators

The WarpX project is developing an exascale application for plasma accelerator research that will pave the way for new breeds of virtual experiments.

Delivering Exascale Machine Learning Algorithms and Tools for Scientific Research

Machine learning, artificial intelligence, and data analytics are converging with high-performance computing to advance scientific discovery.

Ushering in a New Approach to Scientific Computing

CODAR, an Exascale Computing Project co-design center, aims to produce an infrastructure for online data analysis and reduction.

Solving Multiphysics Problems at Scale on Today’s Most Powerful Supercomputers

Technologies known as the Data Transfer Kit (DTK) and ArborX enable researchers to focus more on their science rather than on low-level algorithms in simulations.

Rewriting a Legacy Computational Chemistry Software Package for Larger Simulations and Exascale Speed

A collaborative team is working to get NWChem ready to run on exascale machines and to provide a starting point for future code development.

Developing a Codebase for Deep Learning on Supercomputers to Fight Cancer

Gina Tourassi discusses the Oak Ridge National Laboratory effort within the Exascale Deep Learning–Enabled Precision Medicine for Cancer (CANDLE) project.

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