Projects

The unprecedented power grid evolution poses a monumental challenge in optimizing grid planning.

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

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

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 the first unified autotuning framework that seamlessly integrates programmer-directed and compiler-directed autotuning

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 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.

Designing and delivering novel algorithms, compiling techniques, scheduling tools, and linear algebra approaches for chemical sciences