As the volume and velocity of data generated by scientific experiments increase, research success in a growing number of scientific fields depends on the ability to analyze data rapidly.
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.
The goal of the NoLoSS project is to conduct a detailed assessment of the potential roles and benefits of in-system storage in extreme-scale computational science.
Developing a computationally advanced coupled climate-energy model to investigate the challenges posed by the interactions of weather-climate scale variability with energy and related sectors
Accelerating delivery of an exascale computing ecosystem to provide breakthrough modeling and simulation to address the nation’s most critical challenges
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.
Exploring machine learning projects, ranging from algorithm and software development to applications in science and the environment, to facilitate discovery of new scientific insights
SAGE will build a national research infrastructure of new sensors that support programmable edge computers and machine learning within an interconnected cyberinfrastructure, spanning multiple major science instruments
