Next-Generation Near-Data Processing Architectures

Exploring Julia at the Large Scale

Practitioners of high-performance parallel computing have long sought better programming models and languages to ease the task of writing programs for large-scale systems. However, there is an undeniable tension that exists between extreme performance and developer friendliness.

Coordinated Resource Management for Cloud-based Specialized Operating Systems

Containerization has recently gained significant interest among cloud providers and users due to its ease of deployment and lightweight virtualization capabilities. The key feature of these approaches is the sharing of a single Linux OS instance among each active container environment.

Towards a Practical Ecosystem of Specialized OS Kernels

REU Site: Collaborative Research: BigDataX: From theory to practice in Big Data computing at eXtreme scales

NSF Award CCF-1757964; $333,106; February 2018 through January 2021. This project is in collaboration with Ioan Raicu at IIT as well as Kyle Chard and Aaron Elmore at the University of Chicago.

CSR: Small: Collaborative Research: Flexible Resource Management and Coordination Schemes for Lightweight, Rapidly Deployable OS/Rs

CNS Award CNS-1718252; $249,771 (Collaborative total: $499,735); August 2017 through July 2020. This project is a collaborative effort with Jack Lange at the University of Pittsburgh. Also see here. Current cloud systems leverage either heavy-weight virtualization (running applications inside full-fledged virtual machines (VMs) with their own operating systems) or containers (light-weight software environments that share a single underlying operating system).

CRI: II-NEW: MYSTIC: Programmable Systems Research Testbed to Explore a Stack-WIde Adaptive System fabriC

NSF Award CNS-1730689; $1,000,000; July 2017 through June 2020. This project is a collaborative effort with Ioan Raicu and Xian-He Sun at IIT. This project will build a testbed for experimenting with reconfigurable communication and Input/Output subsystems to conduct low-level systems research.

ConCORD: Easily Exploiting Memory Content Redundancy Through the Content-aware Service Command