
![]() Sun-Yuan Kung IEEE Fellow, Princeton University, USA |
Bio: Sun-Yuan Kung was born in Taiwan on January 2, 1950. He received the B.S. in Electrical Engineering from the National Taiwan University in 1971; M.S. in Electrical Engineering from the University of Rochester in 1974; and Ph.D. in Electrical Engineering from Stanford University in 1977. From 1977 to 1987, he was on the faculty of Electrical Engineering-Systems at the University of Southern California. In 1984, he was a Visiting Professor at Stanford University and later in the same year, a visiting professor at the Delft University of Technology. Since September 1987, he has been a Professor in the Department of Electrical Engineering, Princeton University. He currently serves on the IEEE Technical Committees on VLSI Signal Processing and Neural Networks and an Editor-in-Chief of Journal of VLSI Signal Processing. Kernel Machine for Visualization and Classification of Big Data When: 10:40 Aug. 24 Where: Ballroom Abstract:
Big data has many divergent types of sources, from physical (sensor/IoT) to social and cyber (web) types, rendering it messy and, imprecise, and incomplete. The intensive computing need in big data calls for special hardware and software technologies for parallel and/or distributed computing systems, wit architectural platform closely coupled with the novel and error-tolerant data mining technologies. This talk will attempt a balanced coverage between the theoretical foundation, algorithmic innovation, and architectural codesign. |
![]() Jack Dongarra IEEE Fellow and ACM Fellow, University of Tennessee, USA |
Bio: Jack Dongarra received a Bachelor of Science in Mathematics from Chicago State University in 1972 and a Master of Science in Computer Science from the Illinois Institute of Technology in 1973. He received his Ph.D. in Applied Mathematics from the University of New Mexico in 1980. He worked at the Argonne National Laboratory until 1989, becoming a senior scientist. He now holds an appointment as University Distinguished Professor of Computer Science in the Computer Science Department at the University of Tennessee and holds the title of Distinguished Research Staff in the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL); Turing Fellow at Manchester University; an Adjunct Professor in the Computer Science Department at Rice University; and a Faculty Fellow of the Texas A&M University's Institute for Advanced Study. He is the director of the Innovative Computing Laboratory at the University of Tennessee. He is also the director of the Center for Information Technology Research at the University of Tennessee which coordinates and facilitates IT research efforts at the University. Architecture-aware Algorithms and Software for Peta and Exascale Computing When: 13:30 Aug. 24 Where: Ballroom Abstract: In this talk we examine how high performance computing has changed over the last 10-year and look toward the future in terms of trends. These changes have had and will continue to have a major impact on our software. Some of the software and algorithm challenges have already been encountered, such as management of communication and memory hierarchies through a combination of compile--time and run--time techniques, but the increased scale of computation, depth of memory hierarchies, range of latencies, and increased run--time environment variability will make these problems much harder. |
![]() Ruqian Lu 中国科学院院士 Chinese Academy of Sciences and Academy of Mathematics and Systems Science (CAS), China |
Bio: Ruqian Lu is a professor of computer science of the Institute of Mathematics, Academy of Mathematics and Systems Science, at the same time an adjunct professor of Institute of Computing Technology, Chinese Academy of Sciences and Peking University. He is also a fellow of Chinese Academy of Sciences. His research interests include artificial intelligence, knowledge engineering, knowledge based software engineering, formal semantics of programming languages and quantum information processing. He has published more than 180 papers and 10 books. He has won two first class awards from the Chinese Academy of Sciences and a National second class prize from the Ministry of Science and Technology. He has also won the 2003 Hua Loo-keng Mathematics Prize from the Chinese Mathematics Society and the 2014 lifetime achievements award from the China’s Computer Federation.[Download Bio] Lecture Topic:Combining Process Algebra with Logic Programming When: 8:30 Aug. 24 Where: Ballroom Abstract: This talk presents Knorc - a calculus for KNowledge based ORChestration, which is a conservative extension of the Orc calculus. Orc is, as claimed by its authors, a language for wide area computation and has been developed at University of Dallas. It is simple and powerful with site calls as program units and four combinators to compose them. There was quite a lot of following up works alone this line. Knorc is yet another extension of Orc in direction of knowledge processing. The main new ingredient is logic programming whose combination with process algebra is a major technical challenge to the design of Knorc. Besides introducing new possibilities of implementing site calls, the advantages of this combination include better structuredness of programs, separation of knowledge content from control flow and reusability of knowledge. The second main ingredient is the availability of a set of different parallel programming paradigms, which makes Knorc a process algebra not only with logic programming facilities, but also with powerful parallel logic programming facilities. In particular, it is possible to do massive parallel programming in Knorc. The third main ingredient is the introduction of a specific data type - the abstract knowledge sources to increase its knowledge processing power. While Orc has no data types at all, several extension works in the literature have introduced different data types, e.g. the XML data type. The introduction of abstract knowledge sources makes Knorc a language based on Open World Assumption, rather than Closed World Assumption. We have formalized the syntax and semantics of Knorc. A first implementation of Knorc is underway. |
![]() Tarek El-Ghazawi IEEE Fellow, George Washington University, USA |
Bio: Tarek El-Ghazawi is a Professor in the Department of Electrical and Computer Engineering at The George Washington University, where he leads the university-wide Strategic Academic Program in High-Performance Computing. His research interests include high-performance computing, computer architecture, reconfigurable computing and parallel programming. Exploiting Hierarchical Locality for Productive Extreme Computing When: 10:40 Aug. 25 Where: Room 1D Abstract: Modern high-performance computers are characterized with massive hardware parallelism and deep hierarchies. Hierarchical levels may include cores, dies, chips, and nodes to name a few. Locality exploitation at all levels of the hierarchy is a must as the cost of data transfers can be high. Programmer’s knowledge and the expressivity of locality-aware programming models such as the Partitioned Global Address Space (PGAS) can be very useful. However, locality awareness can come at a high cost. In addition, asking programmers to worry about expressing locality relations at multiple architecture hierarchy levels is detrimental to productivity and systems and hardware must provide adequate support for exploiting hierarchical locality. |