The CEED co-design center is a focused team effort to develop the next-generation discretization software and algorithms that will enable a wide range of finite element applications to run efficiently on future hardware.

Co-design Center for Online Data Analysis and Reduction at the Exascale (CODAR) will address a growing disparity between simulation speeds and I/O rates rendering it infeasible for HPC and data analytic applications to perform offline analysis.

A widening gap between computational and I/O performance, deeper memory and storage hierarchies with complex organization, and massive levels of parallelism (e.g., billions of cores) are but a few of the challenges to data management at the exascale.

The goal of this project is to enable exascale computational science applications to interact conveniently and efficiently with storage through abstractions that match their data models.

The goal of the CODES project is use highly parallel simulation to explore the design of exascale storage architectures and distributed data-intensive science facilities.

Developing and integrating new and modified features into Legion to achieve high performance at the exascale

Understanding the molecular basis of key protein interactions; developing predictive models for drug response; and extraction of information from millions of cancer patient records to determine optimal cancer treatment strategies.

This project seeks to enable the ongoing MPI-related research and development work at Argonne, with the overall goal of enabling MPICH and its derivatives to run effectively at exascale.

This project explores efficient techniques for data movement on heterogeneous memory architectures, where applications can move data from any memory segment to any other memory segment.

The xSDK4ECP project is working to enable the seamless combined use of diverse, independently developed software packages as needed by ECP applications.

EZ is a lossy compressor for scientific datasets respecting user-set error controls.

Delivering a high-quality, robust implementation of OpenMP

Developing improved simulations of supernovae explosions and neutron-star mergers