Our research projects vary in size, scope, and duration, but they share a focus on developing tools and methods that help LLNL deliver on its missions to the nation and, more broadly, advance the state of the art in scientific HPC. Projects are organized here in three ways: Active projects are those currently funded and regularly updated. Legacy projects are no longer actively developed. The A-Z option sorts all projects alphabetically, both active and legacy.
Umpire is a resource management library that allows the discovery, provision, and management of memory on next-generation architectures.
This open-source file system framework supports hierarchical HPC storage systems by utilizing node-local burst buffers.
Collecting variants in low-level hardware features across multiple GPU and CPU architectures
Upgraded with the C++ programming language, VBL provides high-fidelity models and high-resolution calculations of laser performance predictions.
A high-fidelity, specialized code solves partial differential equations for plasma simulations.
Veritas provides a method for validating proxy applications to ensure that they capture the intended characteristics of their parents.
This project advances research in physics-informed ML, invests in validated and explainable ML, creates an advanced data environment, builds ML expertise across the complex, and more.
Researchers develop innovative data representations and algorithms to provide faster, more efficient ways to preserve information encoded in data.
This project constructs coarse time grids and uses each solution to improve the next finer-scale solution, simultaneously updating a solution guess over the entire space-time domain.