Scalable and Efficient Data Management and Analysis for Exascale Computing
報告題目：Scalable and Efficient Data Management and Analysis for Exascale Computing
時 間：2018年8月30日（周四）上午10:00
地 點：環(huán)保園425會議室
報告人：Prof. Gary Liu, New Jersey Institute of Technology
摘要：With increasing fidelity and resolution, scientific applications at Exascale will generate large volumes of data. These data need to be stored, pre-processed, analyzed, and visualized very efficiently, so that the time to gain insights from data can be minimized. Conventional data management strategies are simplistic, and can result in huge performance bottlenecks at Exascale for both data storage and analysis.
In this talk, I will discuss scalable and efficient data management strategies that can reduce these bottlenecks for applications running at scale (e.g., 100,000-core). I will present new techniques that reduce I/O interference in a massively parallel and multi-user environment, without forcing operating system or application level changes. I will then present PreData, a new paradigm that can couple simulations and analytics more efficiently by processing data in-memory and in a streaming fashion. In the end I will briefly introduce my work that combines phase identification and statistical modeling to generate compact and high-fidelity benchmarks for performance evaluations on new HPC systems.
報告人簡介：Dr. Gary Liu is an Assistant Professor in the ECE department at NJIT, and holds a Joint Faculty Appointment with Oak Ridge National Laboratory. Prior to that, he was a Staff Scientist with the Computer Science and Mathematics Division at Oak Ridge National Laboratory. His research interests include Big Data in data-intensive science, high-performance computing, and high-speed networking. In particular, he has done extensive research on scalable data storage and analysis solutions on emerging architectures for HPC applications. His sole research products have been adopted by more than twenty HPC applications in fusion energy, high-energy physics, cancer research, quantum physics, material science, turbine engine design, weather modeling, and etc., for production purposes. He is currently leading and co-leading a few research projects funded by Department of Energy and National Science Foundation.
Dr. Liu received his Ph.D. in Computer Engineering from University of New Mexico, Albuquerque, NM in 2008, and was awarded outstanding graduate of the ECE Department at UNM. He was the distinguished employee of Computing and Computational Science Directorate at ORNL in 2012. He received R&D 100 award as a principle investigator in 2013 for his contributions to adaptable I/O systems for Big Data applications. Dr. Liu has authored and co-authored technical articles on premier conferences such as ACM SIGMETRICS, HPDC, and SC, and his paper was a best paper finalist in ICCCN'08.