LLNL and University of Utah researchers have developed an advanced, intuitive method for analyzing and visualizing complex data sets.

Researchers are developing a standardized and optimized operating system and software for deployment across Linux clusters to enable HPC at a reduced cost.

UMap uniquely exploits the prominent role of complex memories in today’s servers and offers new capabilities to directly access large memory-mapped datasets.

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.

zfp is an open-source C/C++ library for compressed floating-point and integer arrays that support high throughput read and write random access.