A process can voluntarily set memory protections to different portions of its address space. As threads in a process share the same address space, they are equally bound to its protections. We explore the concept of parallel memory permissions, a powerful technique that allows multiple threads to execute in parallel while having different permissions to the same address space, and we show it may be implemented on commodity hardware without requiring special hardware primitives.
This talk will provide an overview of techniques developed in my group to enable robots to react rapidly in the face of changes in the environment when manipulating objects. Learning is guided by observing humans’ elaborate manipulatory skills. I will stress how important it is to model the various ways with which humans perform the same task. This multiplicity of solutions is the key to generate robust and flexible robotic controllers capable of adapting their strategies in the face of unexpected changes in the environment.
Resource sharing is vital to improving efficiency and amortizing cost in high-performance computer systems. Within modern data centers, users selfishly pursue individual performance without regard for others or the system. To address this challenge and study the strategic behaviors of self-interested users, I turn to algorithmic economics and game theory. In my thesis, I rethink resource management in computer architecture and systems, constructing mechanisms that are robust to strategic behavior. First, I describe novel methods to manage shared power supplies in modern data centers.
New Faculty: Kristin Stephens-Martinez
Sudeepa Roy: Making Sense of “Big Data” Databases
As numbers and facts continue to accumulate in today’s world of big data, a growing challenge is how to sift through the reams of data for relevant discoveries. Enter Sudeepa Roy, assistant professor of computer science. Roy is a database researcher who is creating new ways to help mine enormous data sets for meaning. More
Panigrahi Receives NSF CAREER Award
Debmalya Panigrahi has received a CAREER Award from the National Science Foundation for a project entitled "CAREER: New Directions in Graph Algorithms." Total funding will be $515,998 over 5 years. The award will support Panigrahi's research into fundamental problems in graph algorithms seeking generic solutions for core algorithmic challenges in modern networks: efficiency at scale, uncertainty and impreciseness of network requirements, and correlation effects. This is NSF's most prestigious award in support of junior faculty. More