Hybrid Runtimes for Compiled Dataflows

We observe that the OS and database communities face a similar challenge: how do we optimize systems to exploit the characteristics of specialized hardware without sacrificing the flexibility of general-purpose designs?

CS 562: Virtual Machines

Past Iterations: Fall ‘19 Fall ‘18 Fall ‘17

Modeling Speedup in Multi-OS Environments

Prospects for Functional Address Translation

Towards a Practical Ecosystem of Specialized OS Kernels

CS 450: Operating Systems

Past Iterations: Fall ‘20 Spring ‘19

An Evaluation of Asynchronous Software Events on Modern Hardware

CSR: Medium: Collaborative Research: Interweaving the Parallel Software/Hardware Stack

NSF Award CNS-1763612; $305,578 (Collaborative total: $1.2M); September 2018 through August 2021. This project is a collaborative effort with Peter Dinda, Simone Campanoni, and Nikos Hardavellas at Northwestern University. Also see here.

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).