Accelerating Data Science
Gustavo Alonso (ETH Zurich)

Bio: Gustavo Alonso is a professor at the Department of Computer Science of ETH Zurich (ETHZ) in Switzerland, where he is a member of the Systems Group. Gustavo has a M.S. and a Ph.D. in Computer Science from UC Santa Barbara. Before joining ETH, he was at the IBM Almaden Research Center. His research interests encompass almost all aspects of systems, from design to run time. His applications of interest are distributed systems and databases, with an emphasis on system architecture. Current research is related to multi-core architectures, large clusters, FPGAs, and big data, mainly working on adapting traditional system software (OS, database, middleware) to modern hardware platforms.
Gustavo is a Fellow of the ACM and of the IEEE.

Abstract: Data science or big data, whatever one wants to call it, raises important challenges in terms efficient processing. One the one hand, the application demands are becoming more stringent (more data, more complex analysis, faster results, larger workloads, etc.). On the other hand, hardware and computing platforms are in a complex phase with little stability in terms of architectures and lacking an overall direction. In this talk I will discuss the problem, arguing that there is an opportunity for specialized designs departing from general purpose systems. I will illustrate the point with examples from our research and then show how we are exploiting reconfigurable hardware (FPGAs) to explore a wide range of architectural designs, new algorithms for data processing, and redesigning the entire system stack to better support data science. The talk will conclude with a number of ideas on how the database community can contribute to the development of new hardware and how to orchestrate a more coherent, collective research agenda.