High Performance Computing

High Performance Computing (HPC) integrates high end hardware with highly tuned software to solve complex scientific problems. HPC simplifies and accelerates substantial computational components in a number of essential fields, such as genome sequencing and molecular dynamics in computational biology or cyber physical domains. HPC also applies data driven and AI techniques to diagnose and solve performance challenges in HPC systems themselves. HPC research at CISE focuses on improving performance, reducing cost, and making the systems more energy efficient by applying intelligent and interdisciplinary methods. Research areas cross-cut design automation (EDA), computer architecture, computer systems, and applied machine learning.

Meeting Tomorrow’s Sustainability Demands Today

When it comes to high-powered computers, increased energy consumption and lack of reliability continue to be problems researchers are trying to overcome. Unfortunately, some of the best current methods for achieving these goals have their flaws. One example is seen in server consolidation, during which resources are shared across multiple applications and virtual CPUs. Without […]

CAREER: 3D Stacked Systems for Energy-Efficient Computing: Innovative Strategies in Modeling and Runtime Management

Energy efficiency is a central issue in computing. In large-scale computing clusters, operational and cooling costs impose significant sustainability challenges. Embedded systems run increasingly complex, performance demanding workloads, making the well-known energy management policies inadequate. High power densities also cause high on-chip temperatures and large thermal variations, both of which degrade system reliability. The research […]

CPS: Synergy: Data Driven Intelligent Controlled Sensing for Cyber Physical Systems

The goal of this project is to develop the foundations of a control and optimization science base for sensor networks viewed as complex systems operating in an uncertain and potentially adverse environment. The approach taken is a combination of addressing fundamental research issues while maintaining a focus on a specific target application domain, a manufacturing […]