Dartmouth Events

Abstract: We are now firmly in the era of big data, with scientific computing, data mining, social networks, and business management collecting and processing large data-sets to make intelligent decisions about our society and the way we interact with the world. Modern computer systems must not only collect all this data, but must also compute on it quickly and efficiently. At the same time, computer systems must remain easy to program. I will show that achieving efficiency and programmability is challenging, and requires the seamless operation of important abstractions in the systems stack. One such abstraction, virtual memory, is critical to the performance and management of big data systems. I will show, however, that modern virtual memory mechanisms struggle to cope with the massive data-sets prevalent in server and cloud deployments, and are insufficient (and even absent) for emerging hardware accelerators like GPUs. In response, my work architects an efficient virtual memory system for today's computing landscape. Through careful hardware and OS co-design, I will show how to make make big-data systems easier to program, while improving their performance on important classes of workloads (e.g., data mining, deep learning, face detection, graph processing, etc.) by 2-5x.

Bio: Abhishek Bhattacharjee is an Associate Professor in the Department of Computer Science at Rutgers University. His research interests are on designing high-performance and energy-efficient systems. He received a PhD from Princeton University in 2010, and the NSF CAREER award in 2013. His past work has been nominated for the Best Paper Award at MICRO '15, and been included in Micro's Top Picks in Computer Architecture journal in '15. Most recently, he has been awarded the CV Starr fellowship by the Princeton Neuroscience Institute to further his interests at the intersection of neuroscience and computer systems.