School of CSE Seminar Series: Giulia Guidi

Speaker: Giulia Guidi, assistant professor at Cornell University
Date and Time: November 15, 2:00-3:00 p.m.
Location: Scheller 201
Host: Helen Xu and CSE GSA

Title: Overcoming Parallelism Challenges in Data Analytics Using Sparse Linear Algebra

Abstract: The diverse and non-trivial challenges of parallelism in data analytics require computing infrastructures that go beyond the demand of traditional simulation-based sciences. The growing data volume and complexity have outpaced the processing capacity of single-node machines in these areas, making massively parallel systems an indispensable tool. However, programming on high-performance computing (HPC) systems poses significant productivity and scalability challenges. It is important to introduce an abstraction layer that provides programming flexibility and productivity while ensuring high system performance. As we enter the post-Moore's Law era, effective programming of specialized architectures is critical for improved performance in HPC. As large-scale systems become more heterogeneous, their efficient use for new, often irregular and communication-intensive data analysis computation becomes increasingly complex. In this talk, we discuss how sparse linear algebra can be used to achieve performance and scalability on extreme-scale systems while maintaining productivity for emerging data-intensive scientific challenges.

Bio: Giulia Guidi is an assistant professor in the Department of Computer Science and a graduate field faculty in the Department of Computational Biology and the Center for Applied Math at Cornell. She received her Ph.D. in Computer Science from UC Berkeley. She works in the field of high-performance computing for large-scale computational sciences. She is interested in developing algorithms and software infrastructures on parallel machines to speed up data processing without sacrificing programming productivity and to make high-performance computing more accessible. Dr. Guidi received the 2024 SIAM Activity Group on Supercomputing Early Career Prize for her pioneering work bridging high-performance computing and computational biology.