CAREER: Towards Efficient Accelerated Cloud Data Centers

Emerging large-scale compute-intensive applications, such as machine learning and big data analytics, have led to the wide-spread adoption of compute accelerators, such as Graphical Processing Units (GPUs), in cloud data centers. However, the existing cloud management software stack introduces many layers of abstraction that strip away application characteristics and hardware architectural details leading to inefficient and uncoordinated cloud management policies.

This can lead to slow down of application performance and under-utilization of hardware resources, which ultimately impacts the data center's total cost of ownership. This project will design efficient accelerated cloud data centers that are performance-efficient, resource-efficient, and cost-efficient. The planned research has three main goals: (1) develop software frameworks to measure and identify the causes of inefficiencies in accelerated cloud data centers, (2) design inter-accelerator communication-aware cloud management policies, and (3) design accelerator-assisted interconnect topologies.

The success of this project can improve application performance, improve hardware resource utilization, and reduce energy consumption; leading to greener data centers and reduced carbon footprint. In addition, this project will enable data centers to cost-efficiently scale computational power to keep pace with the growing societal demands of emerging machine learning and artificial intelligence applications.

The tools and frameworks developed in this project will be made publicly available to facilitate the efficient integration of compute accelerators in cloud data centers. These new tools and frameworks will form the foundation for new course development, undergraduate research opportunities, and outreach efforts to train and prepare a new generation of engineers who will utilize compute accelerators and cloud resources as a first-class design choice.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.