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Seminar: At the Synergstic Intersection of Parallel Computing, Data Analytic, and Machine Learning

Wu Feng

Professor, Viginia Tech

Friday, September 20
11:15am - 12:15pm
2150 Togersen Hall


With processor clock speeds plateauing around 3GHz by the mid-2000s (due to the  excessive heat being generated by higher clock speeds), the computing industry was forced to embrace parallel computing as a way to continue to improve performance. By the mid-2010s, parallel computing was ubiquitous (e.g., iPhone 6s) while the overall rate of performance improvement of a computing core had slowed from doubling every 1.5 years (1985-2005) to doubling only every 20 years (2015-today). Similarly, over this past decade, our digital universe of big data has grown from 1.2 zettabytes (i.e., sextillion bytes) to nearly 40.0 zettabytes, indicating a doubling of data size every 2 years. Thus, the rate of growth in big data is far outstripping the rate at which computing can (brute-force) compute on the data. As a consequence, we have turned to the co-design of architecture, software, and in particular, algorithms to more efficiently and intelligently compute on the data via algorithmic re-factoring and machine learning, respectively.


Wu Feng is an Elizabeth & James Turner Fellow and Professor of Computer Science at Virginia Tech (VT), where he directs the Synergy Lab and serves as a VT site director for the NSF Center for Space, High-Performance, and Resilient Computing (SHREC) and director of the Synergistic Environments for Experimental Computing (SEEC) Center. In addition, he holds appointments in the Department of Electrical & Computer Engineering, Health Sciences, and Biomedical Engineering and Mechanics. His research area encompasses parallel and distributed computing, ranging from architecture to middleware and tools to applications, with the goal of enabling scientists and engineers to focus on their science and engineering rather than on the computer science and engineering. For more about his lab's and centers' research as well as the associated students, staff, and faculty, please visit,, and