Performance Engineering for Scalable AI and Big Data - Xuhao Chen

Title: Performance Engineering for Scalable AI and Big-data; Abstract: AI applications are computationally expensive and hard to scale, which poses great challenges in computer system design. In this talk, Xuhao will introduce his approach called cross-stack performance engineering, to address this challenge. This approach involves performance optimization techniques and automation methods across different layers of the system stack, including algorithms, software and hardware. He will share insights gained from his experiences in building systems tailored for graph pattern mining (GPM), an important set of algorithms in database and data mining. The first system is Scale-GPM, an algorithm and software codesigned GPM system. Next he will discuss Pangolin, the first GPU-accelerated software programming system dedicated to GPM. Complementing Pangolin is FlexMiner, a dedicated hardware accelerator engineered to further enhance the efficiency of GPM. Throughout the talk, he will showcase compelling results to underscore the effectiveness and its great potential of the cross-stack approach. Bio: Xuhao Chen is a Research Scientist at MIT CSAIL. Dr. Chen is broadly interested in parallel systems and architectures, with a focus on AI and big-data applications. His recent work aims to make AI scalable by designing efficient algorithms, software systems and hardware accelerators. His work has been published in VLDB, OSDI, ISCA, MICRO, ICS, etc. https://www.csail.mit.edu/person/xuhao-chen