Distributed Systems for Computer Graphics
Computer graphics has yet to take advantage of the large-scale distributed systems of the past decade. With the exception of rendering, few graphics systems today are distributed. Instead, they run on powerful servers or, in the best case, a small cluster. Algorithms are designed for single hosts because distributing them is extremely hard. Unlike big data systems, which can logically partition data across keys, graphical systems typically have much more complex dependencies which are hard to distribute. However, the locality in these systems is not arbitrary, as it is typically bound to to a geometric representation of the problem. We are researching large-scale distributed systems that take advantage of this implicit locality to provide real-time, high performance graphics whose scale cannot be served by a few hosts. This research involves virtual world services such as queries and lookup, content simplification designed to load over a wide area network, and a runtime for distributed simulation.
This work is funded by the National Science Foundation (NeTS-ANET Grants #0831163 and #0831374) and conducted in conjunction with the Intel Science and Technology Center -- Visual Computing.
Wireless Networking and Full Duplex Wireless
We research how to build robust, efficient wireless networks. Our work focuses on experimental and scientific results from real-world networks: how do they behave, why, and how can one leverage these behaviors to build better systems? This work includes detailed experimental studies of wireless networks, mathematical models to describe the behavior we observe, protocols which build on this deeper understanding, and novel radio designs for full duplex wireless that will enable a new generation of protocols.
This research has supported by generous gifts from DoCoMo Capital, the National Science Foundation under grants #0832820, #0831163, #0846014 and #0546630, the King Abdullah University of Science and Technology (KAUST), Microsoft Research, scholarships from the Samsung Scholarship Foundation, a Stanford Graduate Fellowship and a Stanford Terman Fellowship.
Embedded Sensing Systems
We research new programming languages, operating systems, and software for embedded devices, such as home automation networks, smart meters, and wireless sensors, as well as personal health devices such as Up and FitBit. Embedded systems are difficult computer platforms for programmers to work with. The need to meet hard real-time deadlines, fit programs into a small amount of memory, be robust to failures, and conserve energy wherever possible. Furthermore, ultra-low power systems have very different hardware, lacking abstractions such as virtualization and cache hierarchies. Our work focuses on software abstractions that allow you to build large, complex applications.
This work was supported by generous gifts from Intel Research, DoCoMo Capital, Foundation Capital, the National Science Foundation under grants #0615308 and #0846014, the King Abdullah University of Science and Technology (KAUST), Microsoft Research, scholarships from the Samsung Scholarship Foundation and a Stanford Terman Fellowship. Our scientific work was supported a National Science Foundation awards BCS-0947132, DE-AR-0000018, grants #0615308 and #0846014, a Branco Weiss fellowship, National Institute of Child Health and Human Development Award 1K01HD051494, and National Institutes of Health Grant GM28016.
Energy Efficient Computing
Energy is the limiting resource in a huge range of computing systems, from embedded sensors to mobile phones to data centers. We research how to design and build computer systems to manage energy and minimize its consumption. This work includes wireless sensing deployments to measure where energy goes in building-scale computer system, software techniques to track energy at microjoule accuracy on embedded systems, and operating systems for phones that make energy a first-class abstraction.
This work was supported by the Department of Energy ARPA-E program under award number DE-AR0000018, generous gifts from DoCoMo Capital, the National Science Foundation under grants #0832820, #0831163, #0846014 and #0546630, the King Abdullah University of Science and Technology (KAUST), Microsoft Research, scholarships from the Samsung Scholarship Foundation, a Stanford Graduate Fellowship and a Stanford Terman Fellowship.