Discrete

DarkNet Cyber Resilience investigates the impacts of cyber stress and its thresholds of impact on alternative timing signals for the US national energy grid.

North American Energy Resilience Model provides the next generation planning infrastructure for the US national energy grid using a cloud-based real-time, interoperable, and federated modeling, simulation, and visualization technology.

Our formulation of a new AI/ML-based, real-time computational framework aims to learn and flag defects in software as early as the time of commit in the developers’ repositories.

Discrete Event Modeling of the Electric Grid is a new non-equilibrium, transient analysis model and solver that is indispensable for new advancements in grids with high renewable penetration.

Our novel Digital Twin Framework (DTF) is designed to improve resilience of critical infrastructure systems by continuously comparing the infrastructure state with automatically generated, AI/ML-based, real-time digital-twin simulation of the system.

Cloned simulations of epidemiological outbreaks like COVID-19 can now rapidly evaluate millions of what-if scenarios at national and world scales, and efficiently exploit 1000s of GPUs.

Cloned eXecution of simulations is a powerful decision-making system to incrementally evaluate millions of what-if scenarios and scale to 1000s of GPUs.

NetWarp is a novel time-synchronized virtual machine(VM)-based parallel simulation framework that accurately lifts the devices and the network communications to a virtual time plane while retaining full fidelity.

Rocks3D-HPC is an efficient parallelization of the Rocks3D high-fidelity earth moving equipment simulation based on the Discrete Element Method implemented using FORTRAN, OpenMP multi-threading, and message passing interface (MPI) for high performance computing.

Real-time Simulations is a simulation framework for fast evaluation of national emergency scenarios such as hurricanes, evaluated with large data streams and real-time computation on the latest hardware platforms on the edge.

This seminar conveys some directions and technical ideas being pursued by the Discrete Computing Systems Group of the Computer Science and Mathematics Division, in collaboration with others at the lab. Some of the scalable computational tools ready for applying to challenging computational problems are presented. Actual working codes ready for customization to COVID-related efforts are described, which are built for scaling to supercomputing platforms such as Summit.