Jan 28 (Tue) @ 10am: "Pushing the Boundaries of Modern Application-Aware Computing Stacks,” Christina Giannoula, U. of Toronto
Location: Engineering Science Building (ESB), Room 2001
Abstract
Modern computing systems encounter significant challenges related to data movement data movement in applications, such as data analytics and machine learning. Within a compute node, the physical separation of the processor from main memory necessitates retrieving data through a narrow memory bus. In big-data applications running across multiple nodes, data must be exchanged via narrow network interconnects. This movement of data —both within and across compute nodes— causes significant performance and energy overheads in modern and emerging applications. Moreover, today’s general-purpose computing stacks overlook the particular data needs of individual applications, missing crucial opportunities for untapped performance optimization.
In this talk, I will present a cross-stack approach to designing application-aware computing stacks for cutting-edge applications, enabling new synergies between algorithms, systems software, and hardware. Specifically, I will demonstrate how integrating fine-grained application characteristics —such as input features, data access and synchronization patterns— across the layers of general-purpose computing stacks allows for tailoring stack components to meet the application’s specific data needs. This integration enables the stack components to work synergistically to reduce unnecessary or redundant data movements during application execution. I will present a few of my research contributions that propose hardware and software solutions for emerging applications, such as deep learning, and by capitalizing on the emerging processing-in-memory paradigm. Finally, I will conclude by outlining my future plans to design application-adaptive and sustainable computing stacks to significantly enhance performance and energy efficiency in cutting-edge applications.
Bio
Christina Giannoula is Postdoctoral Researcher at the University of Toronto, working with Professors Gennady Pekhimenko, Andreas Moshovos and Nandita Vijaykumar. She is an affiliated senior researcher with the SAFARI research group at ETH Zurich, working with Prof. Onur Mutlu, and is also a senior researcher at CentML company, where she advises research engineers on system design for deep learning. Her research lies at the intersection of computer architecture, systems software and parallel computing, with a focus on high performance, energy efficiency, and programmability for emerging applications. Her current research interests include hardware-software co-design for cutting-edge applications, emerging memory technologies such as processing-in-memory, and systems for machine learning. Christina received her Ph.D. in 2022 from the School of Electrical and Computer Engineering (ECE) at the National Technical University of Athens (NTUA), Greece. She has been recognized with several awards and fellowships for her Ph.D. research, including a three-year Ph.D. Fellowship from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT) and a PhD award (Sep 2021-Oct 2022) from the Foundation for Education and European Culture (IPEP). Moreover, her Ph.D. thesis received the Iakovos Giurunlian NTUA award for the doctoral thesis with the highest industrial impact in 2022. For her Postdoctoral research, she has received research grants from the Vector Institute for Artificial Intelligence and has been recognized as a Rising Star in EECS (Electrical Engineering and Computer Sciences) and a Rising Star in MLSys (Machine Learning Systems) in 2024. Christina also serves as the social media editor for the ACM SIGMICRO organization. For more information, please visit her website at https://cgiannoula.github.io/.
Hosted by: The ECE Department
Submitted by: Megan Ashley <mmashley@ucsb.edu>
