High-Performance Computing Systems for Large-Scale Graph Analytics
Dr. Howie Huang, George Washington University
Big data are ubiquitous and graph-based datasets are especially interesting with broader impacts in social networks, biological networks, and cybersecurity. In this talk, I will discuss a number of our recent projects (Enterprise SC’15, iBFS SIGMOD’16, G-Store SC’16, Graphene FAST’17, and Falcon ATC’17) and share our experiences in designing and developing high-performance graph algorithms and systems. In particular, I will discuss several novel techniques on addressing the computational and I/O challenges in graph computing. Furthermore, I will present our ongoing work on utilizing these graph systems for understanding and analyzing complex network data.
About the speaker
Dr. Howie Huang is a Professor of Computer Engineering and the Director of the X-Computing Lab (XCLab) at the George Washington University. Motivated by the needs of big data and cybersecurity applications, he works at the intersection of algorithms, computer architecture and systems, with recent research focus on developing high-performance computing and machine learning techniques tailored for large-scale graph datasets. His work on big graph traversal has ranked highly on both the Graph500 and Green Graph500 benchmarks, which measure the performance and energy efficiency of the most powerful data-intensive supercomputers in the world. Dr. Huang is a recipient of the prestigious National Science Foundation CAREER Award, NVIDIA Academic Partnership Award, Comcast Technology Research and Development Fund Award, and IBM Real Time Innovation Faculty Award. His research won two awards (Finalist and Honorable Mention) at the DARPA Graph Challenge at HPEC'17, the Best Paper Award Nomination at NVMSA'17, the ACM Undergraduate Student Research Competition Winner at SC'12, a Best Student Paper Finalist at SC'11, the Best Poster Award at PACT'11, and a High-Performance Storage Challenge Finalist at SC'09. He received a PhD in Computer Science from the University of Virginia.
Date & Time
Wednesday, August 8, 2018 - 11:00