STONY BROOK, NY, October 20, 2021 – Artificial intelligence (AI) and machine learning (ML) are powerful computer-generated tools that help accelerate and advance scientific discovery and data analyses, but operational challenges behind them are often daunting. Zhenhua Liu, PhD, has received an Early CAREER Award from the National Science Foundation (NSF) to develop data centers for AI/MI jobs in a way that allocates the right amount of resources to each job and placing them on the most efficient hardware.
Ultimately, the research has the potential to improve the efficiency of costly computer systems and help transform operations and education using AI and ML.
“The project will develop an adaptive framework for systems consisting of multiple types of hardware and workloads (heterogeneity) where some of the hardware and workloads can be reconfigured to accelerate real-time AI and ML without hurting other workloads,” says Liu, an Associate Professor in the Departments of Applied Mathematics and Statistics and Computer Science. “Our framework automatically detects, profiles, and analyzes both workloads and accelerators on the fly. Based on the information, it adaptively reconfigures them to match resource capabilities with workload needs.”
Liu also says that global and local optimization will be used to accommodate multiple types of workloads. In this process his research will factor in configuring, partitioning, placement, scheduling and executional models for each workload.
For more details about Liu’s specific research related to the award, see this story.
The NSF award is for more than $500,000 and runs thru the end of August 2026. For more details about the award itself and an abstract, see this NSF webpage.