Heterogeneous computing has advanced our application performance to a new level that is previously out of reach. For example, with CPU-GPU heterogeneous parallelism, training a neural network can be 100x faster than CPUs alone. However, writing a program that utilizes heterogeneous computing resources is not an easy job because we need to manage a lot of technical details, such as programming abstraction, scheduling, and concurrency control.
As a result, this talk will introduce a new task-parallel programming system we established to streamline the building of high-performance computing applications. I will talk about key innovations in our system, including a novel programming model and a learning-based runtime, that offer programmer productivity and performance portability. Finally, I will demonstrate how we applied our system to accelerate both classical and quantum computing problems. So far, our system has been downloaded over a million times, being used by many academic and industrial projects.
Dr. Huang is an assistant professor in the ECE Department at the University of Utah. He received his Ph.D. from the ECE Department at the University of Illinois at Urbana-Champaign and his BS/MS from the CS Department at Taiwan’s National Cheng Kung University. His research group has been creating software systems to streamline the building of high-performance computing applications, including machine learning, computer-aided design, and quantum computing.
Dr. Huang has received several awards for his research contributions, including ACM SIGDA Outstanding Ph.D. Dissertation Award, NSF CAREER Award, and Humboldt Research Fellowship Award.